ppxscal/embedding_network huggingface dataset:
An Algorithm to Solve the Partition into Perfect Matchings Problem in Halin Graphs
Authors: Yunting Lu, Dingjun Lou
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In this paper, we consider whether the vertices of graph G can be partitioned into K subsets V 1, V 2, ... V K so that for each i ¿ {1,...,K}, the subgraph induced by V i is a perfect matching where K ≤ ¿ V ¿. It is known that it is an NP complete problem in general graphs. In this paper, we restrict the problem in Halin graphs and give an algorithm to find the minimum value of K (2 ≤ K ≤ 4) in Halin graphs. The time complexity is O(n).
Using Data Mining to Build Integrated Discrete Event Simulations
Authors: David A. Holland
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

Building a system from disparate software requires analysis to establish commonality of code. The ability of a data mining tool to extract repeating functional structures is the first step to reduce exploration, save development time, and re-use software components. This case study looks specifically at the application of graph-based data mining algorithms to code re-factoring. After writing a module to obtain a graph representation of a discrete event model, we built a tool around the University of Washington's SUBDUE package to find recurring patterns of logic. This resulted in cleaner code and increased awareness of code re-use.
An efficient iris recognition system based on modular neural networks
Authors: H. Erdinc Kocer, Novruz Allahverdi
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

In this paper, we propose a neural network based iris recognition approach by analyzing iris patterns. The iris recognition system consists of iris localization, feature extraction and classification of the iris images. Hough transforms were used for localizing the iris region; Cartesian to polar coordinate transform was used for transforming the ring shaped iris image to the rectangular shape. Then, histogram equalization was applied to the iris image for making the shapes in image more distinctive. Average absolute deviation (AAD) algorithm was used for feature extraction in this approach. In matching process, Multi-Layered Perceptron (MLP) and Modular Neural Networks (MNN) are applied to the iris feature vector for classifying the iris images. In fact, this research is focused on measuring the performance of MNN in iris recognition system compared with Multi-Layered Perceptron (MLP) neural network. The gray-level iris images, experimented in this work, were obtained from Institute of Automation Chinese Academy of Science (CASIA) iris images database and Departments of Informatics University of Beira Interior (UBIRIS) iris images database. Experimental results are given in the last stage of this paper.
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
Authors: Sandeepkumar Satpal, Sunita Sarawagi
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabeled data from the target domain to train a model that maximizes likelihood over the training sample while minimizing the distance between the training and target distribution. Our focus is conditional probability models used for predicting a label structure y given input x based on features defined jointly over x and y. We propose practical measures of divergence between the two domains based on which we penalize features with large divergence, while improving the effectiveness of other less deviant correlated features. Empirical evaluation on several real-life information extraction tasks using Conditional Random Fields (CRFs) show that our method of domain adaptation leads to significant reduction in error.
Dynamic Path Management with Resilience Constraints under Multiple Link Failures in MPLS/GMPLS Networks
Authors: Jong Tae Park, Jae Wook Nah, Wee Hyuk Lee
Venue: IEEE Transactions on Dependable and Secure Computing (2008)

Most previous research on MPLS/GMPLS recovery management has focused on efficient routing or signaling methods from single failures. However, multiple simultaneous failures may occur in large-scale complex virtual paths of MPLS/GMPLS networks. In this paper, we present a dynamic MPLS/GMPLS path management strategy in which the path recovery mechanism can rapidly find an optimal backup path which satisfies the resilience constraints under multiple link failure occurrences. We derived the conditions to test the existence of resilience-guaranteed backup path, and developed a decomposition theorem and backup path construction algorithm for the fast restoration of resilience-guaranteed backup paths, for the primary path with an arbitrary configuration. Finally, simulation results are presented to evaluate the performance of the proposed approach.
Off-line recognition of realistic Chinese handwriting using segmentation-free strategy
Authors: Tong-Hua Su, Tian-Wen Zhang, De-Jun Guan, Hu-Jie Huang
Venue: Pattern Recognition (2009)

Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.
A Novel Off-Line Signature Verification Based on Adaptive Multi-resolution Wavelet Zero-Crossing and One-Class-One-Network
Authors: Zhiqiang Ma, Xiaoyun Zeng, Lei Zhang, Meng Li, Chunguang Zhou
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper proposes a novel off-line signature verification method based on adaptive multi-resolution wavelet zero-crossing and one-class-one-network classification. First, the horizontal, vertical, 45 degree direction and the 135 degree direction projections of the binarizated signature images are calculated, respectively. The curvature data of the projections are decomposed into multi-resolution signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for verification. At last, one-class-one-network classifier is used to verify the signatures. The signature verification system was experimented on real data sets and the results show the system is very effective.
Colored graph transformation rules for model-driven engineering of multi-target systems
Authors: Adrian Stanciulescu, Jean Vanderdonckt, Tom Mens
Venue: Proceedings of the third international workshop on Graph and model transformations (2008)

Multi-target systems are interactive systems that are aimed at covering multiple contexts of use: by enabling users to carry out their tasks thanks to different input and output interaction modalities, these systems should support multiple computing platforms, multiple users, and multiple environments. This paper introduces a model-driven engineering method for developing user interfaces for such multi-target systems. It relies on a transformational approach that applies graph transformation rules on a graph representation of the models in order to support model-to-model transformation. In order to factor out parts that are common in transformation rules used for similar contexts of use, the notion of colored graph transformation is introduced. Each model element is assigned to a specific color depending on the desired target, here the interaction modality involved. Colored transformation rules based on these colored model elements can be therefore specified and applied in order to produce various user interfaces with different modalities of interaction, depending on the context of use. For this purpose, operations over colored transformation rules are defined: a monocolored transformation rule produces a monomodal user interface for a single context of use (single-target system) while multicolored rules embed capabilities for producing a multimodal user interface for multiple contexts of use (multi-target system). The benefits of using multicolored transformation rules over monocolored ones are obtained in terms of number of rules to specify and to apply, in terms of performance of applying these rules and degree of scalability when a new rule corresponding to a new interaction modality should be introduced.
Entertainment R&D for Defense
Authors: Michael Zyda, John Hiles, Alex Mayberry, Casey Wardynski, Michael Capps, Brian Osborn, Russell Shilling, Martin Robaszewski Margaret Davis
Venue: IEEE Computer Graphics and Applications (2003)

In 1997, the National Research Council issued a report indicating the possibility of joint research between the Department of Defense and the entertainment industry. Since that time, the Moves Institute and other research organizations have adopted the research agenda specified and attempted to fulfill the report's promise. The "America's Army" PC game suite is the first successful collaboration to transition from a research effort to a fully developed Internet-based multiplayer game. In this article, the authors describe the research and development involved, some of the difficulties encountered, the successes made possible by the exploitation of institute-based, PC-game research and development capability, and results of the game's launch in May 2002.
Efficient Hierarchical Group-Oriented Key Establishment and Decryption
Authors: Sigurd Eskeland, Vladimir Oleshchuk
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

In this paper, we present three related and efficient cryptographic schemes for secure communication for hierarchically composed user groups. In such a group, each user is associated with a hierarchical level, so that the group members are divided into two or more hierarchical security classes. Most existing hierarchical cryptographic schemes provide hierarchical deduction of hierarchically arranged predefined keys. In contrast, the first scheme provides secure establishment of hierarchically ordered session keys. Since it prohibits long-term key deduction for underlying (and overlying) classes, such keys do not need to be updated session-wise. An essential security property is that the users can only obtain hierarchical session keys for their own and underlying levels, while it is prevented that a user can obtain hierarchical session keys of overlying levels.This scheme is extended to a hierarchical public key cryptosystem based on the ElGamal cryptosystem, and furthermore to an ElGamal-based threshold decryption scheme. Due to the threshold security requirement, at least t arbitrary group members are required to carry out decryption. The threshold scheme requires only one round of broadcasting in the decryption phase, and is thuswell-suitable for wireless networks.
A Steganalysis Method Based On Quantization Attack
Authors: Jifeng Huang, Qingju Jiao
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

We have addressed the problem of image steganalysis that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. In actual practice, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Statistical steganalysis exploits these irregularities in order to provide the best discrimination between cover and stego images. Attacking the cover-images and stego-images using the quantization method, we can obtained statistically different from embedded-and-quantization attacked images and from quantization attacked-but-not-embedded sources. We have developed a technique based on one-class SVM for discriminating between cover-images and stego-images. Simulation results with the chosen feature set and well known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.
Kernel Method Used for Multi-sensor Information Fusion in Self-Diagnostic Smart Structures
Authors: Jianhong Xie
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Smart structures is a multi-sensor architecture, and multi-sensor information fusion is a key technology used to realize the self-diagnosing damages function for smart structures. Due to a few of intrinsic flaws of traditional neural networks, the fusion algorithms based on kernel learning are progressing rapidly in recent years. Based on the approximately orthonormal property of wavelet kernel function, Least Square Wavelet Support Vector Machine (LS-WSVM) used for information fusion is proposed, and its tuning parameters are optimized by Genetic Algorithm (GA). Based on the feature-level fusion, LS-WSVM fusion algorithm is applied to self-diagnose damages for smart structures, and its performance is evaluated through some tests. The results show that LS-WSVM possesses the higher fusion accuracy, bitter dissemination ability than LS-SVM with Gaussian kernel function under the same conditions, and LS-WSVM can decrease the amount of computation in information fusion. 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
The Complete Flux Scheme for Spherically Symmetric Conservation Laws
Authors: J. H. Thije Boonkkamp, M. J. Anthonissen
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

We apply the finite volume method to a spherically symmetric conservation law of advection-diffusion-reaction type. For the numerical flux we use the so-called complete flux scheme. In this scheme the flux is computed from a local boundary value problem for the complete equation, including the source term. As a result, the numerical flux is the superposition of a homogeneous flux and an inhomogeneous flux. The resulting scheme is second order accurate, uniformly in the Peclet numbers.
Using the Self-Organizing Map (SOM) Algorithm, as a Prototype E-Content Retrieval Tool
Authors: Athanasios S. Drigas, John Vrettaros
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

SOM O.D.I.S.S.E.A.S is an intelligent searching tool using the Self-Organizing Map (SOM) algorithm, as a prototype e-content retrieval tool. The proposed searching tool has the ability to adjust and scale into any e-learning system that requires concept-based queries. In the proposed methodology, maps are used for the automatic replacement of the unstructured, the half structured and the multidimensional data of text, in a way that similar entries in the map are represented near between them. The performance and the functionality of the document organization, and the retrieval tool employing the SOM architecture, are also presented. Furthermore, experiments were performed to test the time performance of a learning algorithm used for the direct creation of teams of terms and texts enabling efficient searching and retrieval of the documents.
Simulation of Time Series Prediction Based on Smooth Support Vector Regression
Authors: Chao Zhang, Pu Han, Guiji Tang, Guori Ji
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Time series analysis and prediction is an important means of dynamic system modelling, but traditional methods of time series prediction such as statistics and artificial neural network (ANN) are not fit for complicated non-linear system. Hence, a new method of support vector regression (SVR) was introduced to solve the prediction problem of complicated time series. For the purpose of reducing complexity of calculation, smooth arithmetic based on SVR was imported to forecast the time series of vibration data collected from turbine system. The result of simulation indicated that smooth support vector regression (SSVR) is obviously superior to ANN method on performance of prediction. Compared with SVR, SSVR has faster speed of convergence and higher fitting precision, which effectively extends the application of support vector machine.Keywords: time series prediction, support vector machine, regression, smooth method, turbine.
Multimedia genre characterisation with fuzzy embedding classifiers
Authors: Alberto Messina, Maurizio Montagnuolo
Venue: Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television (2008)

Multimedia classification is a key issue in modern data management, where the number of available items is dramatically growing and there is an increasing demand for access to distributed multimedia data. Selection by genre is a simple and effective mechanism for most of the users interested in these applications. In this paper, we present a feature extraction architecture and a novel learning algorithm for multimedia genre characterisation. We show how genre classification can be regarded as a sub-case of this general task, for which we give a complete solution. Our extracted features were designed to offer a reduced semantic gap, trying to take into account structural and cognitive content descriptors, rather than low-level features. Our learning algorithm is based on fuzzy set theory, and makes use of fuzzy C-means (FCM) algorithm as the kernel to learn concepts configurations from data. We tested our learning framework on a test database of over 100 hours of TV broadcast programmes belonging to 7 different common genres. Experimental evaluations showed the effectiveness of our approach. Additionally, we compared our technology with neural networks applied on the same task, in terms of training accuracy. We also compared the generalisation performances of our technique with neural networks and support vector machines.
Rough approximation operators on two universes of discourse and their fuzzy extensions
Authors: Tong-Jun Li
Venue: Fuzzy Sets and Systems (2008)

Based on a crisp binary relation between two universes of discourse, one crisp covering and three crisp binary relations on the single universe of discourse are induced in this paper. Based on these induced notions, four pairs of rough approximation operators are formulated so that the approximating sets and the approximated sets are on the same universe of discourse. Furthermore, basic properties of the new approximation operators are investigated, and the relationships among the operators are also examined. Subsequently, conditions under which some or all of these approximation operators are equivalent are obtained. On the other hand, the generalizations of these rough approximation operators are also made in a fuzzy approximation space via a fuzzy implicator I and a triangular norm T. Consequently, four pairs of fuzzy rough approximation operators are constructed. Basic properties and comparison of the fuzzy rough approximation operators are discussed. If I is an R-implicator with respect to a t-norm T, then it is shown that three pairs of the fuzzy rough approximation operators are identical iff the induced fuzzy covering is a fuzzy T-partition.
Relationship Web: Blazing Semantic Trails between Web Resources
Authors: Amit P. Sheth, Cartic Ramakrishnan
Venue: IEEE Internet Computing (2007)

The authors argue that relationships are at the heart of semantics, and, as such, they envision a Web of relationships to relate content across Web resources. Under this powerful new paradigm, information access over the Web would switch from a mere document-retrieval operation to an information framework that supports insight elicitation and semantic analytics over Web resources.
Search Strategies for Rectangle Packing
Authors: Helmut Simonis, Barry O'Sullivan
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Rectangle (square) packing problems involve packing all squares with sizes 1 ×1 to n ×n into the minimum area enclosing rectangle (respectively, square). Rectangle packing is a variant of an important problem in a variety of real-world settings. For example, in electronic design automation, the packing of blocks into a circuit layout is essentially a rectangle packing problem. Rectangle packing problems are also motivated by applications in scheduling. In this paper we demonstrate that an "off-the-shelf" constraint programming system, SICStus Prolog, outperforms recently developed ad-hoc approaches by over three orders of magnitude. We adopt the standard CP model for these problems, and study a variety of search strategies and improvements to solve large rectangle packing problems. As well as being over three orders of magnitude faster than the current state-of-the-art, we close eight open problems: two rectangle packing problems and six square packing problems. Our approach has other advantages over the state-of-the-art, such as being trivially modifiable to exploit multi-core computing platforms to parallelise search, although we use only a single-core in our experiments. We argue that rectangle packing is a domain where constraint programming significantly outperforms hand-crafted ad-hoc systems developed for this problem. This provides the CP community with a convincing success story.
Modeling and simulation of net centric system of systems using systems modeling language and colored Petri-nets: A demonstration using the global earth observation system of systems
Authors: Madwaraj Rao, Sreeram Ramakrishnan, Cihan Dagli
Venue: Systems Engineering (2008)

Understanding the operations of a large &lsquo;&lsquo;net-centric system-of-systems&rsquo;&rsquo; requires in-depth knowledge of the interfaces and the interactions among the various systems, subsystems, and components. Architectural modeling can help in reducing the complexity involved in designing large networked systems. This paper demonstrates a modeling approach for network centric systems. An example of such a complex system is the Global Earth Observation System of Systems (GEOSS)&mdash; a system for monitoring and collecting information related to Earth's resources. The GEOSS is an evolving complex network centric system. The modeling of the GEOSS has been demonstrated using the Systems Modeling Language (SysML). In this paper, architecture representation using SysML demonstrates an object-oriented approach of model development. This paper discusses issues related to architecture description, development, presentation, and integration for the chosen domain. This paper also highlights some of the differences between SysML, which is used to model a wide range of systems, and Unified Modeling Language (UML), which is primarily used to model information systems only. Finally, in order to synthesize an executable model from the static views developed using SysML, Colored Petri-nets (CP-nets) have been used. The executable model, constructed using CP nets, is used to validate the architecture against the static model. Overall, this research defines a methodology to model and simulate complex network centric system of systems in order to understand and simulate their behavior using a scenario based approach. &copy; 2008 Wiley Periodicals, Inc. Syst Eng