ppxscal/embedding_network huggingface dataset:
Faster and More Focused Control-Flow Analysis for Business Process Models Through SESE Decomposition
Authors: Jussi Vanhatalo, Hagen Völzer, Frank Leymann
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

We present a technique to enhance control-flow analysis of business process models. The technique considerably speeds up the analysis and improves the diagnostic information that is given to the user to fix control-flow errors. The technique consists of two parts: Firstly, the process model is decomposed into single-entry-single-exit (SESE) fragments, which are usually substantially smaller than the original process. This decomposition is done in linear time. Secondly, each fragment is analyzed in isolation using a fast heuristic that can analyze many of the fragments occurring in practice. Any remaining fragments that are not covered by the heuristic can then be analyzed using any known complete analysis technique.We used our technique in a case study with more than 340 real business processes modeled with the IBM WebSphere Business Modeler. The results suggest that control-flow analysis of many real process models is feasible without significant delay (less than a second). Therefore, control-flow analysis could be used frequently during editing time, which allows errors to be caught at earliest possible time.
Hamiltonian Connected Line Graphs
Authors: Deng-Xin Li, Hong-Jian Lai, Ye-Hong Shao, Mingquan Zhan
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Thomassen conjectured [8] that every 4-connected line graph is hamiltonian. An hourglass is a graph isomorphic to K 5 ¿ E(C), where C is a cycle of length 4 in K 5. In [2], it is shown that every 4-connected line graph without an induced subgraph isomorphic to the hourglass is hamiltonian connected. In this note, we prove that every 3-connected, essentially 4-connected hourglass-free line graph is hamiltonian connected.
One-Class Classification by Combining Density and Class Probability Estimation
Authors: Kathryn Hempstalk, Eibe Frank, Ian H. Witten
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.
Efficient MRI Reconstruction Using a Hybrid Framework for Integrating Stepwise Bayesian Restoration and Neural Network Models in a Memory Based Priors System
Authors: D. A. Karras
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The goal of this paper is to present a new image restoration methodology for extracting Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Support Vector Machine (SVM) models in a memory based Bayesian priors system and a novel Bayesian restoration, in the problem of MR image reconstruction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to make measurement time smaller by minimizing scanning trajectories. It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, applying to the k-space data a novel Bayesian restoration method based on progressively approximating the unknown image, involving a neural model based priors system with memory specifically aimed at minimizing first order image derivatives reconstruction errors. More specifically, it is demonstrated that SVM neural network techniques could construct efficient memory based Bayesian priors and introduce them in the procedure of a novel stepwise Bayesian restoration. These Priors are independent of specific image properties and probability distributions. They are based on training SVM neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based Bayesian priors system with memory is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction and aims at minimizing image derivatives and image reconstruction errors. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the Integrated Bayesian MRI reconstruction involving simple SVM models priors as well as memory based priors minimizing image reconstruction errors instead of its derivatives errors, by the traditional Bayesian MRI reconstruction as well as by the pure Neural filter based reconstruction approach.
Integrating Trust in Virtual Organisations
Authors: Ramón Hermoso, Holger Billhardt, Sascha Ossowski
Venue: Coordination, Organizations, Institutions, and Norms in Agent Systems II: AAMAS 2006 and ECAI 2006 International Workshops, COIN 2006 Hakodate, Japan, May 9, 2006 Riva del Garda, Italy, August 28, 2006. Revised Selected Papers (2007)

Organisational models cannot only be used to structure multiagent systems but also to express behaviour constraints for agents in open environments. However, sometimes these behaviour constraints cannot be exhaustively enforced, and some agents may transgress the norms put forward by a Virtual Organisation. This poses an additional burden on agents, as they cannot be sure that their acquaintances will behave as prescribed. Trust and reputation mechanisms are of particular relevance to this respect, as they are commonly used to infer expectations of future behaviour from past interactions.In this paper we argue that, on the one hand, the a priori structure of Virtual Organisations can be useful to improve the efficiency of trust and reputation mechanisms, and that, on the other hand, such mechanisms provide relevant information for agents that are part of Virtual Organisations. For this purpose, we identify relevant aspects of existing organisational (meta-)models, and outline a reputation mechanism for Virtual Organisations that integrates these aspects. The dynamics of this mechanism is illustrated by an example.
Efficient Adaptive Strategy for Solving Inverse Problems
Authors: M. Paszyński, B. Barabasz, R. Schaefer
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

The paper describes the strategy for efficient solving of difficult inverse problems, utilizing Finite Element Method (FEM) as a direct problem solver. The strategy consists of finding an optimal balance between the accuracy of global optimization method and the accuracy of an hp-adaptive FEM used for the multiple solving of the direct problem. The crucial relation among errors was found for the objective function being the energy of the system defining the direct problem. The strategy was applied for searching the thermal expansion coefficient (CTE) parameter in the Step-and-flash Imprint Lithography (SFIL) process.
Adaptability of the TRSIM Model to Some Changes in Agents Behaviour
Authors: Alberto Caballero, Juan A. Botia, Antonio Gómez-Skarmeta
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

Trust and reputation models are very useful tools to assist decision making process within agents. They can help to represent and to approximately predict the behaviour of the agents in a system. Trust and reputation values can be used to recognize the agents with a good-expected performance. This way, trust and reputation models offer an adaptive mechanism to guide interactions between agents. In this paper we study the behaviour of TRSIM model when it is applied to consumer-provider scenario. We present several experimental evidences related with the stability of the model for different types of requirements of the consumer, and recognition of different types of providers. Also, we study the ability of the model to adapt to behavioural changes of provider agents.
Determining Knots with Quadratic Polynomial Precision
Authors: Zhang Caiming, Ji Xiuhua, Liu Hui
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

A new method for determining knots in parametric curve interpolation is presented. The determined knots have a quadratic polynomial precision in the sense that an interpolation scheme which reproduces quadratic polynomials would reproduce parametric quadratic polynomials if the new method is used to determine knots in the interpolation process. Testing results on the efficiency of the new method are also included.
Tamper-Resistant, Application-Aware Blocking of Malicious Network Connections
Authors: Abhinav Srivastava, Jonathon Giffin
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

Application-level firewalls block traffic based on the process that is sending or receiving the network flow. They help detect bots, worms, and backdoors that send or receive malicious packets without the knowledge of users. Recent attacks show that these firewalls can be disabled by knowledgeable attackers. To counter this threat, we develop VMwall, a fine-grained tamper-resistant process-oriented firewall. VMwall's design blends the process knowledge of application-level firewalls with the isolation of traditional stand-alone firewalls. VMwall uses the Xen hypervisor to provide protection from malware, and it correlates TCP or UDP traffic with process information using virtual machine introspection. Experiments show that VMwall successfully blocks numerous real attacks--bots, worms, and backdoors--against a Linux system while allowing all legitimate network flows. VMwall is performant, imposing only a 0---1 millisecond delay on TCP connection establishment, less than a millisecond delay on UDP connections, and a 1---7% slowdown on network-bound applications. Our attack analysis argues that with the use of appropriate external protection of guest kernels, VMwall's introspection remains robust and helps identify malicious traffic.
Reducing Kernel Development Complexity in Distributed Environments
Authors: Adrien Lèbre, Renaud Lottiaux, Erich Focht, Christine Morin
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

Setting up generic and fully transparent distributed services for clusters implies complex and tedious kernel developments. More flexible approaches such as user-space libraries are usually preferred with the drawback of requiring application recompilation. A second approach consists in using specific kernel modules (such as FUSE in Gnu/Linux system) to transfer kernel complexity into user space.In this paper, we present a new way to develop kernel distributed services for clusters by using a cluster wide consistent data management service. This system, entitled kDDM for "kernel Distributed Data Management", offers flexible kernel mechanisms to transparently manage remote accesses, cache and coherency. We show how kDDM simplifies distributed kernel developments by presenting the design and the implementation of a service as complex as a fully symmetric distributed file system.The innovative approach of kDDM has the potential to boost the development of distributed kernel services because it relieves the developers of the burden of dealing with distributed protocols and explicit data transfers.
A Method for Capturing the Finger-Vein Image Using Nonuniform Intensity Infrared Light
Authors: Yanggang Dai, Beining Huang, Wenxin Li, Zhuoqun Xu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In the finger vein authentication, one problem is that the variable finger brightness makes the veins unclear. For the same reason, it is difficult to get the intact finger-vein pattern which contains more features. In this paper, we developed an experimental rule to adjust the intensity of the light, which can solve the problem mentioned. We propose three criterions, the standard deviation of the gray level, total length of the vein and the number of bifurcations, to judge the quality of the image. In our experiment, our method makes the brightness of the finger more uniform, thus the standard deviation of the gray level decrease by 48.4% on average. The numbers of two features, the veins and its bifurcations, increase by 44.1% and 31.4% respectively. Using our method, we have built a finger vein image database which has 600 images.
A Systematic Comparison of Different HMM Designs for Emotion Recognition from Acted and Spontaneous Speech
Authors: Johannes Wagner, Thurid Vogt, Elisabeth André
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

In this work we elaborate the use of hidden Markov models (HMMs) for speech emotion recognition as a dynamic alternative to static modelling approaches. Since previous work on this field does not yet define a clear line which HMM design should be prioritised for this task, we run a systematic analysis of different HMM configurations. Furthermore, experiments are carried out on an acted and a spontaneous emotions corpus, since little is known about the suitability of HMMs for spontaneous speech. Additionally, we consider two different segmentation levels, namely words and utterances. Results are compared with the outcome of a support vector machine classifier trained on global statistics features. While for both databases similar performance was observed on utterance level, the HMM-based approach outperformed static classification on word level. However, setting up general guidelines which kind of models are best suited appeared to be rather difficult.
Improved algorithms for recognizing p-Helly and hereditary p-Helly hypergraphs
Authors: Mitre C. Dourado, Min Chih Lin, Fábio Protti, Jayme L. Szwarcfiter
Venue: Information Processing Letters (2008)

A hypergraph H is set of vertices V together with a collection of nonempty subsets of it, called the hyperedges of H. A partial hypergraph of H is a hypergraph whose hyperedges are all hyperedges of H, whereas for V^'@?V the subhypergraph (induced by V^') is a hypergraph with vertices V^' and having as hyperedges the subsets obtained as nonempty intersections of V^' and each of the hyperedges of H. For p>=1 say that H is p-intersecting when every subset formed by p hyperedges of H contain a common vertex. Say that H is p-Helly when every p-intersecting partial hypergraph H^' of H contains a vertex belonging to all the hyperedges of H^'. A hypergraph is hereditary p-Helly when every (induced) subhypergraph of it is p-Helly. In this paper we describe new characterizations for hereditary p-Helly hypergraphs and discuss the recognition problems for both p-Helly and hereditary p-Helly hypergraphs. The proposed algorithms improve the complexity of the existing recognition algorithms.
Towards the Automatic Construction of Conceptual Taxonomies
Authors: Dino Ienco, Rosa Meo
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

In this paper we investigate the possibility of an automatic construction of conceptual taxonomies and evaluate the achievable results. The hierarchy is performed by Ward algorithm, guided by Goodman-Kruskal ¿ as proximity measure. Then, we provide a concise description of each cluster by a keyword representative selected by PageRank.The obtained hierarchy has the same advantages - both descriptive and operative - of indices on keywords which partition a set of documents with respect to their content.We performed experiments in a real case - the abstracts of the papers published in ACM TODS in which the papers have been manually classified into the ACM Computing Taxonomy (CT). We evaluated objectively the generated hierarchy by two methods: Jaccard measure and entropy. We obtained good results by both the methods. Finally we evaluated the capability to classify in the categories of the two taxonomies showing that KH provides a greater facility than CT.
A scalable and distributed model for self-organization and self-healing
Authors: Michael Rubenstein, Wei-Min Shen
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

As the ability to produce a large number of small, simple robotic agents improves, it becomes essential to control the behavior of these agents in such a way that the sum of their actions gives rise to the desired overall result. These agents are modeled as homogeneous, distributed robots, with only one simple short range sensor. Our simple agents are tasked to form and hold a desired swarm shape, independent of the total number of agents. If this shape is damaged by the removal of some of the agents, the remaining agents will recover the former shape, but on a smaller scale. These shapes can also have a pattern such as a picture or drawing displayed on them by controlling the individual robots color, symbolically representing the differentiation of agents within the swarm. This pattern will resize to fit the existing swarm. With the ability to synchronize in time, the swarm gains the ability to change the pattern displayed, resulting in a moving image.
Active Shape Models and Evolution Strategies to Automatic Face Morphing
Authors: Vittorio Zanella, Héctor Vargas, Lorna V. Rosas
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Image metamorphosis, commonly known as morphing, is a powerful tool for visual effects that consists of the fluid transformation of one digital image into another. There are many techniques for image metamorphosis, but in all of them there is a need for a person to supply the correspondence between the features in the source image and target image. In this paper we use the Active Shape Models and Evolution Strategies to perform the metamorphosis of face images in frontal view automatically.
Mii & you
Authors: Katerina Karoussos
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This paper illustrates the concept of ''Mii & You'' workshop which is under the framework of ''The Monumental Data project'' that aims to empower cultural memory via the usage of Information & Communication Technologies and especially with sensor type communication and mobile devices. Such practice has to include a set of aesthetic information covering a person's cultural data. We are using Wii console as an interface and we are working on Wii's Mii Plaza editor in order to formulate cultural identities which are moving perpetually and arbitrarily into an abstract/digital place via wireless systems.
Computer-aided three-dimensional texture analysis for lung characterization using mdct images
Authors: Eric A. Hoffman, David Eichmann, Milan Sonka
Venue:  (2007)

With the increasing ability of imaging technologies to provide true volumetric image data there is a need to advance the field of computer-aided texture classification to take advantage of 3-D feature information. This dissertation extends 2-D texture definitions to 3-D and demonstrates superiority of 3-D vs. 2-D approaches to texture characterization with particular emphasis on assessing the lung field in multidetector-row CT (MDCT). In the research presented here, we extend 2-D texture features to 3-D and add 6 new features to significantly enhance lung pathology classification. We present a new method allowing for region-by-region classification of irregularly shaped objects such as the lung and show the effect of volumes of interest (VOIs) extending beyond the boarders of the object. We have demonstrated the added benefit of eliminating intervening structures in the VOIs such as airways or blood vessels. We demonstrate the robustness of what we term the 3-D Adaptive Multiple Feature Method (3D-AMFM) when trained with a mixture of scanners and reconstruction protocols. In a series of studies using the 3D-AMFM we demonstrate that 3-D texture features provide a more sensitive and specific assessment of lung pathology than 2-D and our expanded 3-D feature set can successfully differentiation parenchymal pathologies associated with both emphysema and interstitial lung diseases. The choices of MDCT brand and convolution kernel types do not affect classification significantly. The new algorithm can classify pathology as "mixed" when two pathologies appear in the same VOI, such as honeycomb and ground-glass patterns. We demonstrate that the 3D-AMFM can serve as a useful second reader, particularly when non-expert readers are evaluating a lung CT scan. We show the ability of the 3D-AMFM to detect early, subtle differences in lung tissues such as normal lung from never smoking volunteers vs. normal appearing lung of smokers who by all clinical measures are normal. We finally demonstrate the ability of the 3D-AMFM to predict regions of lung to become damaged in an animal model of acute respiratory distress, demonstrating the power of this new texture analysis tool in triaging disease, planning interventions and assessing outcomes.
Performance Analysis of R*-Trees with Arbitrary Node Extents
Authors: Yufei Tao, Dimitris Papadias
Venue: IEEE Transactions on Knowledge and Data Engineering (2004)

Existing analysis for R-trees is inadequate for several traditional and emerging applications including, for example, temporal, spatio-temporal, and multimedia databases because it is based on the assumption that the extents of a node are identical on all dimensions, which is not satisfied in these domains. In this paper, we propose analytical models that can accurately predict R*-tree performance without this assumption. Our derivation is based on the novel concept of extent regression function, which computes the node extents as a function of the number of node splits. Detailed experimental evaluation reveals that the proposed models are accurate, even in cases where previous methods fail completely.