ppxscal/embedding_network huggingface dataset:
Defining Secure Business Processes with Respect to Multiple Objectives
Authors: Thomas Neubauer, Johannes Heurix
Venue: Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (2008)

Business processes are of major importance in today’s business environments, and their unimpeded execution is crucial for a company’s success. Since business processes are permanently exposed to a variety of threats, organizations are forced to pay attention to security issues. Although the security of business activities is widely recognized as important, business processes and security aspects are often developed separately and without considering different objectives. This paper proposes a methodology that supports corporate decision makers with the elicitation of security requirements based on business processes, for the analysis of threats and vulnerabilities, and for the selection of appropriate security measures.
Brief paper: Notes on averaging over acyclic digraphs and discrete coverage control
Authors: Chunkai Gao, Jorge Cortés, Francesco Bullo
Venue: Automatica (Journal of IFAC) (2008)

In this paper we study averaging algorithms and coverage control laws in a unified light. First, we characterize the convergence properties of averaging algorithms over acyclic digraphs with fixed and controlled-switching topology. Second, we introduce and study novel discrete coverage control laws, which are useful in practical implementations of coverage strategies. We characterize the close relationship of the novel discrete control laws with continuous coverage control laws and with averaging algorithms over a class of acyclic digraphs, that we term discrete Voronoi graphs. These results provide a unified framework to model a vast class of distributed optimization problems.
Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning
Authors: Meijun Yang, Qiang Shen
Venue: International Journal of Knowledge-based and Intelligent Engineering Systems (2008)

This paper presents an integrated knowledge-based system, which combines fuzzy rule-based reasoning with case-based reasoning, for turbomachinery diagnosis. By incorporating a case-based reasoning sub-system in a fuzzy rule-based system, the integrated system allows past experience to be applied in a more direct way. This helps improve the diagnostic accuracy of the rule-based system. This approach has been implemented for the specific task of identifying possible causes of observed vibrations in rotating machines, based on the initial work presented in [18]. The ability that the case-based sub-system brings to the integrated system in improving the diagnostic efficacy of the original rule-based system is demonstrated with test results on real cases.
Probabilistic multi-class multi-kernel learning
Authors: Theodoros Damoulas, Mark A. Girolami
Venue: Bioinformatics (2008)

Motivation: The problems of protein fold recognition and remote homology detection have recently attracted a great deal of interest as they represent challenging multi-feature multi-class problems for which modern pattern recognition methods achieve only modest levels of performance. As with many pattern recognition problems, there are multiple feature spaces or groups of attributes available, such as global characteristics like the amino-acid composition (C), predicted secondary structure (S), hydrophobicity (H), van der Waals volume (V), polarity (P), polarizability (Z), as well as attributes derived from local sequence alignment such as the Smith–Waterman scores. This raises the need for a classification method that is able to assess the contribution of these potentially heterogeneous object descriptors while utilizing such information to improve predictive performance. To that end, we offer a single multi-class kernel machine that informatively combines the available feature groups and, as is demonstrated in this article, is able to provide the state-of-the-art in performance accuracy on the fold recognition problem. Furthermore, the proposed approach provides some insight by assessing the significance of recently introduced protein features and string kernels. The proposed method is well-founded within a Bayesian hierarchical framework and a variational Bayes approximation is derived which allows for efficient CPU processing times. Results: The best performance which we report on the SCOP PDB-40D benchmark data-set is a 70% accuracy by combining all the available feature groups from global protein characteristics but also including sequence-alignment features. We offer an 8% improvement on the best reported performance that combines multi-class k-nn classifiers while at the same time reducing computational costs and assessing the predictive power of the various available features. Furthermore, we examine the performance of our methodology on the SCOP 1.53 benchmark data-set that simulates remote homology detection and examine the combination of various state-of-the-art string kernels that have recently been proposed. Contact: theo@dcs.gla.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Venue: Symposium on Principles of Database Systems (2008)

This volume contains the proceedings of the Twenty-Seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2008), held in Vancouver, Canada, on June 9-11, 2008, in conjunction with the 2008 ACM SIGMOD International Conference on Management of Data. The proceedings include one paper based on the keynote address by Peter Buneman, two papers based on the invited tutorials by Mikolaj Bojanczyk and Wenfei Fan, and 28 contributed papers that were selected by the Program Committee from 159 submissions. Most of these papers are preliminary reports on work in progress. While they have been read by program committee members, they have not been formally refereed. Many of them will probably appear in more polished and detailed form in scientific journals. The program committee selected the paper "Estimating PageRank on Graph Streams" by Atish Das Sarma, Sreenivas Gollapudi and Rina Panigrahy for the PODS 2008 Best Paper Award, and the paper "Evaluating Rank Joins with Optimal Cost" by Karl Schnaitter and Neoklis Polyzotis for the PODS 2008 Best Newcomer Award. Warmest congratulations to the authors of these papers. I thank all authors who submitted papers to the symposium, and the members of the program committee for the enormous amount of work they have done. The program committee did not meet in person, but carried out extensive discussions during the electronic PC meeting.
Assessing Wireless Network Dependability through Knowledge Extraction via Decision Trees
Authors: G. Weckman, A. Snow, Preeti Rastogi, M. Rangwala
Venue: Proceedings of the Third International Conference on Systems (2008)

Critical infrastructures such as wireless network systems demand dependability. Dependability attributes reported here include availability, reliability, maintainability and survivability (ARMS). This research uses computer simulation and knowledge extraction to introduce a new approach to measure dependability of wireless networks. Earlier research has used computer simulation for estimating wireless network dependability. This work introduces a new methodology which uses discrete time event simulation in-put/output to train an artificial neural network and then extract knowledge via decision trees. A comparison of decision tree extrac-tion technique results are discussed, including those from neural (TREPAN) and non neural networks (C4.5). Significant insights are gained into increasing wireless infrastructure dependability through such knowledge extraction techniques; however the neural approach is superior from a parsimonious and comprehensibility perspective.
Berge graphs with chordless cycles of bounded length
Authors: Irena Rusu
Venue: Journal of Graph Theory (1999)

A graph is called weakly triangulated if it contains no chordless cycle on five or more vertices (also called hole) and no complement of such a cycle (also called antihole). Equivalently, we can define weakly triangulated graphs as antihole-free graphs whose induced cycles are isomorphic either to C3 or to C4. The perfection of weakly triangulated graphs was proved by Hayward [Hayward, J Combin Theory B. 39 (1985), 200&ndash;208] and generated intense studies to efficiently solve, for these graphs, the classical NP-complete problems that become polynomial on perfect graphs. If we replace, in the definition above, the C4 by an arbitrary Cp (p even, at least equal to 6), we obtain new classes of graphs whose perfection is shown in this article. In fact, we prove a more general result: for any even integer p &ge; 6, the graphs whose cycles are isomorphic either to C3 or to one of Cp, Cp+2, &hellip;, C2p 6 are perfect. &copy; 1999 John Wiley & Sons, Inc. J Graph Theory 32: 73&ndash;79, 1999
Modified sign method for testing the fractality of Gaussian noise
Authors: A. P. Kovalevskii
Venue: Problems of Information Transmission (2008)

Fractal Gaussian noise is a stationary Gaussian sequence of zero-mean random variables whose sums possess the stochastic self-similarity property. If the random variables are independent, the self-similarity coefficient equals 1/2. The sign criterion for testing the hypothesis that the parameter equals 1/2 against the alternative H ¿ 1/2 is based on counting the sign change rate for elements of the sequence. We propose a modification of the criterion: we count sign change indicators not only for the original random variables but also for random variables formed as sums of consecutive elements. The proof of the asymptotic normality of our statistics under the alternative hypothesis is based on the theorem on the asymptotics of the covariance of sign change indicators for a zero-mean stationary Gaussian sequence with a slowly decaying correlation function.
Test suite development with TTCN-3 libraries
Authors: Stephan Schulz
Venue: International Journal on Software Tools for Technology Transfer (STTT) (2008)

This paper presents a methodology for test suite implementation based on Testing and test control notation (TTCN)-3 libraries which has been developed and successfully applied for the implementation of complex, standardized Internet Protocol version 6 (IPv6) conformance test suites at ETSI. It introduces the concept of a TTCN-3 library, discusses benefits and challenges, and examines the effect of some important TTCN-3 language constructs on reuse. Finally, it explains in detail a layered library design approach to test implementation in the context of a IPv6 test suite.
Embedding a Corporate Blogging System in the CRM Solutions
Authors: Narjes Sharif Razavian, Fattaneh Taghiyareh
Venue: Proceedings of the Fifth International Conference on Information Technology: New Generations (2008)

Corporate blogging is a new one-to-one communication method that increases company’s transparency, and customer feedback and trust. The purpose of this paper is to present a solution to embed a corporate blogging system in the customer relationship management systems. Our blogging system categorizes the comments and entries based on automatically detected meta-data, and each of the comments of the proposed categories enters the appropriate functions of the CRM. This close integration provides higher efficiency and effectiveness to the corporation.
Failure Prediction Mechanisms in Cluster Systems
Authors: Mohsen Sharifi, Seyed Ali Hamedi
Venue: Proceedings of the 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies (2008)

Clustering is an important technique for improving the performance and availability of computer systems. The use of cluster systems is also continuously growing because they present excellent features like scalability, high availability and high performance computing. Availability is mainly administered by failure detection and recovery mechanism, including proactive failure mechanisms that try to prevent occurrences of faults. Given the criticality and importance of availability for high performance computing, this paper uniquely surveyes noticeable existing mechanisms for prevention of faults in high availability and high performance computing cluster systems, and presents a comparative overview.
On the monotonicity of an adaptive splitting scheme for two-dimensional singular reaction-diffusion equations
Authors: Abdul Q. M. Khaliq, Qin Sheng
Venue: International Journal of Computer Mathematics (2007)

This paper studies a fully adaptive splitting method for the numerical solution of two-dimensional quenching differential equations. The non-uniform adaptive mesh is established in space and time via standard arc-length formulations. We concentrate on the monotonic property of the numerical algorithm developed since the issue is fundamental for this type of nonlinear singular differential equations. Numerical examples are given to illustrate our computational results as well as the quenching phenomena approximated.
A Novel Scheme for Power Amplifier Predistortion Based on Indirect Learning Architecture
Authors: Ai Bo, Zhong Zhang-Dui, Zhu Gang, Yang Yan, Xu Rong-Tao
Venue: Wireless Personal Communications: An International Journal (2008)

Polynomial predistortion techniques for power amplifier (PA) non-linearities based on indirect learning architecture (IDLA) are widely used. The benefit of the IDLA leaves unnecessary the assumption of a model for PA, corresponding parameters estimation and inverse construction. In this paper, a novel scheme based on IDLA for PA predistortion is proposed. It has better power spectral density (PSD) and relative mean square error (RMSE) performances than the conventional IDLA-based methods especially when the PA non-liearity is more severe. Simulations and theoretical analysis verify the good performances of the proposed scheme.
Using artificial intelligent techniques to build adaptative tutoring systems
Authors: Denysde Medina Sotolongo, Natalia Martínez Sánchez, Zoila Zenaida García Valdivia
Venue: Proceedings of the 2007 Euro American conference on Telematics and information systems (2007)

When a tutoring aims to guide students in the teaching/learning process, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. The Bayesian framework offers a number of techniques for inferring individual's knowledge state from evidence of mastery of concepts or skills. Using Bayesian networks, we have devised the probabilistic student models for MacBay, a tutoring system that is an authoring tool. MacBay's models provide prediction of student's action during teaching/learning process. We combined the Concept Maps and the Bayesian networks in order to obtain a Concept Map with intelligent behavior, where "the intelligence" is considered as the capacity to adapt the interaction to its user's specific needs. In this paper we describe the way in that we do this combination and inference process.
Doppler embolic signal detection using the adaptive wavelet packet basis and neurofuzzy classification
Authors: Yijiao Chen, Yuanyuan Wang
Venue: Pattern Recognition Letters (2008)

The transcranial Doppler (TCD) ultrasound technique is widely applied to detect emboli within the middle cerebral artery (MCA). However, due to the interference of certain artifacts including the probe motion and patient movement, etc. the detection results obtained with the TCD are always obscured. In traditional methods, the spectrogram analysis and/or the wavelet transform are performed to represent Doppler ultrasound signals and extract sensitive characteristics. Unfortunately these features do not have ideal specificity and sensitivity because of limitations on the time and frequency resolution and a lack of an adaptive classifier. In this paper, a new method based on the adaptive wavelet packet basis (AWPB) is used to make a sparse representation of Doppler ultrasound blood flow signals. Different from other dimensionality reduction methods, both the approximation coefficients and the decomposition scales are extracted to represent the signal and sent to the Takagi-Sugeno (T-S) neurofuzzy classifier. The T-S fuzzy inference system can easily combine the linguistic expert rules to make a more robust decision score to characterize emboli. Experiments show that within a 15% confidence interval of the decision score, 99.0% and 97.1% detection rates of emboli can be obtained for simulated and in vivo studies, respectively. The proposed AWPB method and neurofuzzy classification quite outperform traditional methods and are suitable to detect Doppler embolic signals with a high performance.
Goodness-of-fit tests based on a robust measure of skewness
Authors: Guy Brys, Mia Hubert, Anja Struyf
Venue: Computational Statistics (2008)

In this paper we propose several goodness-of-fit tests based on robust measures of skewness and tail weight. They can be seen as generalisations of the Jarque---Bera test (Bera and Jarque in Econ Lett 7:313---318, 1981) based on the classical skewness and kurtosis, and as an alternative to the approach of Moors et al. (Stat Neerl 50:417---430, 1996) using quantiles. The power values and the robustness properties of the different tests are investigated by means of simulations and applications on real data. We conclude that MC-LR, one of our proposed tests, shows the best overall power and that it is moderately influenced by outlying values.
A Novel Migration Algorithm Based-On "States-Balancing in a Distributed Multimedia Services System
Authors: Minglong Zhang, Boqin Feng
Venue: Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering (2008)

In this paper, a novel SLB scheduling algorithm in a geographically distributed VoD system is proposed. Firstly, we define three states based on the current load of a multimedia server, and then analyze the load balancing from the narrow sense and the broad sense so as to introduce the migration arithmetic of “state balancing" which can provide a hierarchic load balancing and the fault tolerance. The experimentation shows that the accessing failure rates and the load variance of this algorithm are the same as the results of the REM, but the ability of fault recovery is much better than REM when the sys is under the normal state, which indicates that this arithmetic can not only provide load balancing within a multimedia server and “state balancing” among the multimedia servers, but also enhance the fault tolerance of the multimedia service system.
Selecting features for object detection using an AdaBoost-compatible evaluation function
Authors: Luka Fürst, Sanja Fidler, Aleš Leonardis
Venue: Pattern Recognition Letters (2008)

This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates.
A derivation of roughness correlation length for parameterizing radar backscatter models
Authors: M. M. Rahman, M. S. Moran, D. P. Thoma, R. Bryant, E. E. Sano, C. D. Holifield Collins, S. Skirvin, C. Kershner, B. J. Orr
Venue: International Journal of Remote Sensing (2007)

Surface roughness is a key parameter of radar backscatter models designed to retrieve surface soil moisture (θS) information from radar images. This work offers a theory-based approach for estimating a key roughness parameter, termed the roughness correlation length (L c). The L c is the length in centimetres from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other. The approach is based on the relation between L c and h RMS as theorized by the Integral Equation Model (IEM). The h RMS is another roughness parameter, which is the root mean squared height variation of a rough surface. The relation is calibrated for a given site based on the radar backscatter of the site under dry soil conditions. When this relation is supplemented with the site specific measurements of h RMS, it is possible to produce estimates of L c. The approach was validated with several radar images of the Walnut Gulch Experimental Watershed in southeast Arizona, USA. Results showed that the IEM performed well in reproducing satellite-based radar backscatter when this new derivation of L c was used as input. This was a substantial improvement over the use of field measurements of L c. This new approach also has advantages over empirical formulations for the estimation of L c because it does not require field measurements of θS for iterative calibration and it accounts for the very complex relation between L c and h RMS found in heterogeneous landscapes. Finally, this new approach opens up the possibility of determining both roughness parameters without ancillary data based on the radar backscatter difference measured for two different incident angles.
Discriminative Learning of Max-Sum Classifiers
Authors: Vojtěch Franc, Bogdan Savchynskyy
Venue: The Journal of Machine Learning Research (2008)

The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and observable variables. Predicting labels as MAP assignments of a Random Markov Field is a particular example of the max-sum classifier. Learning parameters of the max-sum classifier is a challenging problem because even computing the response of such classifier is NP-complete in general. Estimating parameters using the Maximum Likelihood approach is feasible only for a subclass of max-sum classifiers with an acyclic structure of neighbouring pairs. Recently, the discriminative methods represented by the perceptron and the Support Vector Machines, originally designed for binary linear classifiers, have been extended for learning some subclasses of the max-sum classifier. Besides the max-sum classifiers with the acyclic neighbouring structure, it has been shown that the discriminative learning is possible even with arbitrary neighbouring structure provided the quality functions fulfill some additional constraints. In this article, we extend the discriminative approach to other three classes of max-sum classifiers with an arbitrary neighbourhood structure. We derive learning algorithms for two subclasses of max-sum classifiers whose response can be computed in polynomial time: (i) the max-sum classifiers with supermodular quality functions and (ii) the max-sum classifiers whose response can be computed exactly by a linear programming relaxation. Moreover, we show that the learning problem can be approximately solved even for a general max-sum classifier.