ppxscal/embedding_network huggingface dataset:
A Case Study in Sequential Pattern Mining for IT-Operational Risk
Authors: Valerio Grossi, Andrea Romei, Salvatore Ruggieri
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

IT-operational risk management consists of identifying, assessing, monitoring and mitigating the adverse risks of loss resulting from hardware and software system failures. We present a case study in IT-operational risk measurement in the context of a network of Private Branch eXchanges (PBXs). The approach relies on preprocessing and data mining tasks for the extraction of sequential patterns and their exploitation in the definition of a measure called expected risk.
Transmissions in Home Networks
Authors: Rita Puzmanova
Venue: IEEE Distributed Systems Online (2004)

A book review of Home Networking Basis: Transmission Environments and Wired/Wireless Protocols, by Walter Y. Chen. 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
Fuzzy Evolutionary Probabilistic Neural Networks
Authors: V. L. Georgiou, Ph. D. Alevizos, M. N. Vrahatis
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To further improve its performance, a fuzzy class membership function has been incorporated for the weighting of its pattern layer neurons. For each neuron of the pattern layer, a fuzzy class membership weight is computed and it is multiplied to its output in order to magnify or decrease the neuron's signal when applicable. Moreover, a novel scheme for multi---class problems is proposed since the fuzzy membership function can be incorporated only in binary classification tasks. The proposed model is entitled Fuzzy Evolutionary Probabilistic Neural Network and is applied to several real-world benchmark problem with promising results.
EEG to MRI Registration Based on Global and Local Similarities of MRI Intensity Distributions
Authors: Žiga Špiclin, Arne Hans, Frank H. Duffy, Simon K. Warfield, Boštjan Likar, Franjo Pernuš
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

In this paper, a novel method for EEG to MRI registration is proposed. Initial registration is achieved by extracting and matching symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities. Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of 0.48 ±0.33 mm, while with real EEG data an average root-mean-square point-to-surface error of 2.27 ±0.02 mm was obtained.
A Genetic Algorithm for Automatic Packing in Rapid Prototyping Processes
Authors: Weidong Yang, Weiling Liu, Libing Liu, Anping Xu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Rapid prototyping (RP) is an emerging technology for creating physical prototypes from three-dimensional computer aided design models by using the additive process with layers. The loading multiple parts into the building space of RP machines, which is a packing optimization problem, is considered as the most important factor to maximize the utilization of space in the building cylinder and reduce building time. To obtain optimized packing result, a genetic algorithm (GA) is developed and implemented. In this paper, chromosomes consist of two segments: one represent the sequence of parts packing by transposition expression, the other represent the rotation mode of parts. This paper will detail the development of a specific algorithm to solve the conversion between the scanning sequence and part layout in two-dimension packing and three-dimension packing of RP processes. Experiments show that the building space is to be used more productively by this packing method for multiple parts.
DMGrid: A Data Mining System Based on Grid Computing
Authors: Yi Wang, Liutong Xu, Guanhui Geng, Xiangang Zhao, Nan Du
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Researchers in the field of data mining now confront a common problem that data mining tasks are time-consuming in that these tasks have to process large-scale datasets. Grid computing focuses on integrating distributed, heterogeneous and idle computers from the Internet to be a service system with high performance. Thus, it is possible to take advantage of grid computing to provide high performance computation capability to effectively reduce task durations. Here, we have successfully developed DMGrid, a grid handling data mining applications. In DMGrid, it not only considers efficient parallel computing as a crucial aspect, but also takes into account dynamic resource configuration. Unlike many existing data mining grids, DMGrid also provides an engine to execute the algorithm flow specified in an application. Moreover, it offers application execution monitoring. At last, we perform experiments and design two applications: Customer Churning Analysis and Customer Value Analysis through which the feasibility of DMGrid is validated.
Concern-Sensitive Navigation: Improving Navigation in Web Software through Separation of Concerns
Authors: Jocelyne Nanard, Gustavo Rossi, Marc Nanard, Silvia Gordillo, Leandro Perez
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

Traditionally, the use of good techniques to improve software modularity, such as advanced separation of concerns, has no impact in the user experience, for example while navigating Web software. While the intent of these techniques is to simplify evolution and maintenance, navigation design quality is often seen as an unrelated concern. In this paper we present a novel approach for improving navigation in Web applications by using some of the core application's concerns (called navigational concerns) to derive their navigational structure. Using some realistic examples we show that, by carefully using these concerns, we can improve the user experience. Some implementation issues are discussed and a thorough comparison with related ideas in the Web Engineering field is presented.
Haskell session types with (almost) no class
Authors: Riccardo Pucella, Jesse A. Tov
Venue: Proceedings of the first ACM SIGPLAN symposium on Haskell (2008)

We describe an implementation of session types in Haskell. Session types statically enforce that client-server communication proceeds according to protocols. They have been added to several concurrent calculi, but few implementations of session types are available. Our embedding takes advantage of Haskell where appropriate, but we rely on no exotic features. Thus our approach translates with minimal modification to other polymorphic, typed languages such as ML and Java. Our implementation works with existing Haskell concurrency mechanisms, handles multiple communication channels and recursive session types, and infers protocols automatically. While our implementation uses unsafe operations in Haskell, it does not violate Haskell's safety guarantees. We formalize this claim in a concurrent calculus with unsafe communication primitives over which we layer our implementation of session types, and we prove that the session types layer is safe. In particular, it enforces that channel-based communication follows consistent protocols.
Reconstruction Methods for Incomplete Fuzzy Preference Relations: A Numerical Comparison
Authors: Matteo Brunelli, Michele Fedrizzi, Silvio Giove
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

In this paper we compare, by means of numerical simulations, seven different methods for reconstructing incomplete fuzzy preference relations. We consider the case of highly inconsistent preference relations as well as the case of preference relations close to consistency. We compare the numerical results on the basis of the consistency of the reconstructed preference relations.
Combining Densely Sampled Form and Motion for Human Action Recognition
Authors: Konrad Schindler, Luc Gool
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

We present a method for human action recognition from video, which exploits both form (local shape) and motion (local flow). Inspired by models of the human visual system, the two feature sets are processed independently in separate channels. The form channel extracts a dense local shape representation from every frame, while the motion channel extracts dense optic flow from the frame and its immediate predecessor. The same processing pipeline is applied in both channels: feature maps are pooled locally, down-sampled, and compared to a collection of learnt templates, yielding a vector of similarity scores. In a final step, the two score vectors are merged, and recognition is performed with a discriminative classifier. In an evaluation on two standard datasets our method outperforms the state-of-the-art, confirming that the combination of form and motion improves recognition.
Design of 3D Mobile Phones and Application for Visual Communication
Authors: Min-Chul Park, Jung-Young Son
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

This paper discusses realization of 3D mobile phones in respect of acquiring stereo images and displaying them. The parallel and radial types as 3D imaging layouts and autostereoscopic 3D displays are realized and their experimental results are compared. This paper proposes that when a mobile is equipped with a single camera the radial type has an advantage for taking stereo images satisfying fusible 3D images in a short distance because it has a permissible disparity in a arm length specially only. However, when a mobile phone is equipped with a pair of cameras the parallel-type has more advantage in that it offers a large fusible area in providing 3D depth cue to viewers relatively. A slanted parallax barrier generates viewing zone to the viewer without Moiré effect, but the complexity of the structure causes computational burden for image display. To testify 3D mobile visual communication stereo images are transmitted over CDMA mobile networks and the experiment is successfully accomplished keeping an acceptable image quality and 3D effects.
Plenary lecture I: classification with diffuse or incomplete information
Authors: Amaury A. Caballero
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

In many different fields like finance, business, pattern recognition, communication and many other applications, analysts are often faced with the task of classifying items based on historical or measured data. A major difficulty faced by such analysts is that the data to be classified can often be quite complex, with numerous interrelated variables, or incomplete. The time and effort required to develop a model to solve accurately such classification problems can be significant. There could be three major categories that affect directly the performance of the classifier: Ambiguous class labels in the sample data set, Values corrupted by noise or not enough precise sensor measurements, Missing values in the incoming information. Many methods exist for solving the problem: Imputation techniques, Factorial analysis, Decision tree methods, Rule-based methods, fuzzy logic, Neural networks and Bayesian and Dependency Networks. The most important characteristic of a classifier is its generalization ability, permitting to produce decisions based on data not previously seen during the training process. The use of neural networks and fuzzy logic give the analyst a powerful tool for solving the proposed task. The work is focused on analyzing the advantages of these methods, from the point of view of their simplicity and time consuming. Several examples are introduced in order to clarify the presentation.
A semi-adaptive smoothing algorithm in bispectrum estimation with application of spectrum analysis in seizure detection
Authors: Wei Yang
Venue:  (2007)

The Fourier transform is often used to calculate the third-order periodogram, which is an estimate of the bispectrum. However, it has energy leakage at adjacent frequencies, and the third-order periodogram is not a consistent estimator of the bispectrum because the variance does not converge toward zero with increasing sample size. The Kolmogorov-Zurbenko Fourier Transform (KZFT) is an iteration of the regular Fourier transform. It can overcome the energy leakage by k degrees less where k is the number of iterations in the KZFT algorithm. To obtain a consistent spectrum estimation of a stationary time series, smoothing the periodogram across the frequency is often used. To obtain a consistent estimator of the bispectrum, a semi-adaptive smoothing algorithm is developed to smooth the third-order periodogram calculated with the KZFT. The shape of the spectral window is fixed as a square window. The bandwidth of the spectral window is determined by the optimal squared variation which is a pre-specified ad hop percentage of the total squared variation. The algorithm was applied using simulated data. It separated two close frequency components with very different energies in noisy data to estimate the bispectrum. In the application, we developed an outlier detection algorithm for automated seizure detection. The data used was electroencephalogram (EEG) data recorded from patients with epilepsy. Using the training set, we converted the raw EEG data into frequency-based features based upon the spectrum, and used stepwise logistic regression for feature selection. In the resulting multi-dimensional feature space, we derived a frequency heuristic, defined as the direction from the center of the non-seizure data to the center of the seizure data. This direction heuristic was used with two other seizure heuristics: (1) a seizure is a sudden change from baseline activity; and (2) a seizure lasts at least 10 seconds. The algorithm was applied to 46 hours of intracranial EEG data and correctly identified eight out of nine seizures, with only one false positive. Keywords: KZ statistics, KZFT, Third-order periodogram, Bispectrum, Smoothing, Optimal local squared variation, EEG, Seizure detection 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
Personal Computers Aid the Handicapped
Authors: Ware Myers
Venue: IEEE Micro (1982)

Personal computers can be used to improve the quality of life for handicapped people-as well as to recover a wealth of human resources that would otherwise be lost.
Learning SVM with Varied Example Cost: A kNN Evaluating Approach
Authors: Chan-Yun Yang, Che-Chang Hsu, Jr-Syu Yang
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

The paper proposes a model merging a non-parametric k-nearest-neighbor (kNN) method into an underlying support vector machine (SVM) to produce an instance-dependent loss function. In this model, a filtering stage of the kNN searching was employed to collect information from training examples and produced a set of emphasized weights which can be distributed to every example by a class of real-valued class labels. The emphasized weights changed the policy of the equal-valued impacts of the training examples and permitted a more efficient way to utilize the information behind the training examples with various significance levels. Due to the property of estimating density locally, the kNN method has the advantage to distinguish the heterogeneous examples from the regular examples by merely considering the situation of the examples themselves. The paper shows the model is promising with both the theoretical derivations and consequent experimental results.
Modular installations for complex automations
Authors: Lucian Marius Velea, Luige Vladareanu, Zachei Podea, Alida Lia Mariana Velea, Alexandru Gal, Octavian Melinte
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

The paper presents technique performances that implies the ability of generating command installations and operations with a high level of complexity in order to drive technological processes by monitoring all states, motions, positions and the loading of all operation and execution elements. It represents a modern solution for new installations to be manufactured and it will be considered as a method to re-equip the existing installations. The novelty consists in the decentralized and distributed process modular structures achieved by using all of the PLC's facilities, and in achieving interactive systems with the PC. In the following context it has attained a general solution with the aim to achieve installations for complex automations.
Towards Generalised Accessibility of Computer Games Introduction to the Special Thematic Session
Authors: Dominique Archambault, Roland Ossmann, Klaus Miesenberger
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This is the fourth time that a Special Thematic Session on accessible entertainment has been organised at ICCHP and we are beginning to see progress in the field. A decade ago most work focused on how to play computer games with alternative devices; today we are starting to talk about the accessibility of mainstream games.People with disabilities probably make up the single group benefiting the most from Information and Communication Technologies (ICT). Not only does ICT improve their ability to do things which could be done another way, but it makes a real difference to independent living: with the support of Assistive Technology (AT) they are actually able to do things they had no chance of achieving before and this in an independent way. This positively affects many situations in their daily lives, at school as well as at work or home and concerns mobility and other issues.
Local Adaptation of Leak Factor Alpha Based on Mode-Motion-Product of the Base-Layer Macroblocks in FGS Coding with Adaptive Reference
Authors: Yubin Cui, Anni Cai
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

The mode context and motion vectors of the base-layer local macroblocks (MB) are used to estimate the drift when the colocated MBs in the fine granular scalability (FGS) layer are truncated. A parameter called mode-motion-product (MMP) is defined for MBs or 8×8 blocks in the base layer. A novel method for local adaptation of leak factor alpha in adaptive reference FGS (AR-FGS) is proposed based on the derived MMP of the base-layer MB. Experimental results show that the proposed method can obtain an average luminance PSNR gain up to 0.2dB compared to joint video team (JVT)-T062 on the testing conditions specified in JVT-T305.
Soft Decision-Directed Square Contour Algorithm for Blind Equalization
Authors: Liu Shunlan, Dai Mingzeng
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

The recently introduced square contour algorithm (SCA) combines the benefits of the generalized Sato algorithm (GSA) and the constant modulus algorithm (CMA). It implicitly updates phase and is less likely to converge to incorrect solutions. But the SCA has relatively large residual error after the algorithm reaches its steady state for high-order constellations. A new blind equalization algorithm is proposed based on concurrent square contour algorithm (SCA) and soft decision-directed (SDD) adaptation. Like the SCA, the proposed concurrent SCA and SDD algorithm includes phase recovery and offers good convergence characteristics. Simulation results demonstrate that the proposed SCA+SDD algorithm offers practical alternatives to blind equalization of high-order QAM channels and provides significant equalization improvement over the CMA, GSA and SCA.
Cell Bounds in Two-Way Contingency Tables Based on Conditional Frequencies
Authors: Byran Smucker, Aleksandra B. Slavković
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

Statistical methods for disclosure limitation (or control) have seen coupling of tools from statistical methodologies and operations research. For the summary and release of data in the form of a contingency table some methods have focused on evaluation of bounds on cell entries in k-way tables given the sets of marginal totals, with less focus on evaluation of disclosure risk given other summaries such as conditional probabilities, that is, tables of rates derived from the observed contingency tables. Narrow intervals - especially for cells with low counts - could pose a privacy risk. In this paper we derive the closed-form solutions for the linear relaxation bounds on cell counts of a two-way contingency table given observed conditional probabilities. We also compute the corresponding sharp integer bounds via integer programming and show that there can be large differences in the width of these bounds, suggesting that using the linear relaxation is often an unacceptable shortcut to estimating the sharp bounds and the disclosure risk.
Tridiagonalizing Complex Symmetric Matrices in Waveguide Simulations
Authors: W. N. Gansterer, H. Schabauer, C. Pacher, N. Finger
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

We discuss a method for solving complex symmetric (non-Hermitian) eigenproblems Ax = ¿Bx arising in an application from optoelectronics, where reduced accuracy requirements provide an opportunity for trading accuracy for performance. In this case, the objective is to exploit the structural symmetry. Consequently, our focus is on a non-Hermitian tridiagonalization process. For solving the resulting complex symmetric tridiagonal problem, a variant of the Lanczos algorithm is used. Based on Fortran implementations of these algorithms, we provide extensive experimental evaluations. Runtimes and numerical accuracy are compared to the standard routine for non-Hermitian eigenproblems, LAPACK/zgeev. Although the performance results reveal that more work is needed in terms of increasing the fraction of Level 3 Blas in our tridiagonalization routine, the numerical accuracy achieved with the non-Hermitian tridiagonalization process is very encouraging and indicates important research directions for this class of eigenproblems.