ppxscal/embedding_network huggingface dataset:
Plenary lecture II: some IP security issues
Authors: Zoran Bojkovic
Venue: Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (2008)

IP security (IPsec) is a suite of protocols for searing Internet Protocol (IP) communications by authenticating and or encrypting each IP packet in a data stream. IP packets do not have any inherent security. As a result there is no guarantee that a received IP packet is from the claimed sender contains original data that the sender put in it or was not sniffed during transit. IPsec provides a method to protect IP datagrams and is commonly used in Virtual Private Networks (VPNs). It defines a method for specifying the traffic to protect, how that traffic is to be protected and to whom the traffic is sent. From thee point of view of multimedia networks, security is important to be recognized for current and future users and implements. In response to IP security issues, Internet Architecture Board (IAB) included authentication and encryption as necessary security features in the next-generation IP, which has been used as IPv6. Fortunately, these security capabilities were designed to be usable both with the current IPv4 and the IPv6. Following an introduction, this presentation begins by introducing Internet Key Exchange (IKE) protocol. The goal of thus protocol is to establish and maintain shared security parameters and authenticable keys between the two IPsec end points. For both IPv4 and IPv6 the choice of Encapsulating Security Payload (ESP) protocol and Authentication Header (AH) is offered. The IP ESP provides confidentiality, along with optional (but strongly recommended) authentication and integrity protection. The IP AH provides integrity and authentication and integrity protection. The next parts of this lecture cover frameworks for basic security concepts and security technology. The IP security architecture uses the concept of a security association as the basis for building security function into IP. A security association is simply the bundle of algorithms and parameters (such as keys) that is being used to encrypt and authenticate a parameter flow in one direction. In bi-directional traffic, the flows are secured by a pair of security associations. Security technology is a term that relates to the technical methods used to realize security requirements (cryptographic mechanisms, hash schemes, key management methods). Next part of this presentation covers infrastructure for future mobile networks because they will be open to different services and service providers. Also, five seacurity features groups (network access security, network domain security, user domain security, application domain security, visibility and configurability of security) are analyzed. Finally, infrastructure security definitions, requirements and security context together with network operator's security requirements, requirements from user's, network's as well as service's perspective are enclosed.
Improving Adaptiveness in Autonomous Characters
Authors: Mei Yii Lim, João Dias, Ruth Aylett, Ana Paiva
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

Much research has been carried out to build emotion regulation models for autonomous agents that can create suspension of disbelief in human audiences or users. However, most models up-to-date concentrate either on the physiological aspect or the cognitive aspect of emotion. In this paper, an architecture to balance the Physiological vs Cognitive dimensions for creation of life-like autonomous agents is proposed. The resulting architecture will be employed in ORIENT which is part of the EU-FP6 project eCircus. An explanation of the existing architecture, FAtiMA focusing on its benefits and flaws is provided. This is followed by a description of the proposed architecture that combines FAtiMA and the PSI motivational system. Some inspiring work is also reviewed. Finally, a conclusion and directions for future work are given.
Semantic Visualization of Patient Information
Authors: Sonja Zillner, Tamas Hauer, Dmitry Rogulin, Alexey Tsymbal, Martin Huber, Tony Solomonides
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Clinical practice and research rely increasingly on analytic approaches to patient data. Visualization enables the comparative exploration of similar patients, a key requirement in certain clinical decision support systems. Patient data is complex and heterogeneous, may have different formats, reside in various structures and carry different semantics. This makes the comparison and analysis of clinical data a challenging task. Most medical applications visualize patient data without integrating additional semantic information to structure the analysis. Our objective is to map patient data onto relevant fragments of ontologies and inferred ontological structures as a basis for improved patient data visualization, comparison, and analysis. Two visualization scenarios that we have implemented using the patient data acquired in the Health-e-Child project will be presented and their clinical evaluation will be provided.
Scalable, Adaptive Streaming for Nonlinear Media
Authors: Ketan Mayer-Patel, David Gotz
Venue: IEEE MultiMedia (2007)

Streaming linear media objects has become ubiquitous on today's Internet. Interactive, nonlinear media objects, such as large 3D models and visualization databases, have proven difficult to stream. The Channel Set Adaptation (CSA) framework lets clients request custom data flows for interactive applications using standard broadcast or multicast join and leave operations. CSA scales to support large user groups while providing interactive data access to clients.
A Generalized Brezing-Weng Algorithm for Constructing Pairing-Friendly Ordinary Abelian Varieties
Authors: David Freeman
Venue: Proceedings of the 2nd international conference on Pairing-Based Cryptography (2008)

We give an algorithm that produces families of Weil numbers for ordinary abelian varieties over finite fields with prescribed embedding degree. The algorithm uses the ideas of Freeman, Stevenhagen, and Streng to generalize the Brezing-Weng construction of pairing-friendly elliptic curves. We use our algorithm to give examples of pairing-friendly ordinary abelian varieties of dimension 2 and 3 that are absolutely simple and have smaller ρ-values than any previous such example.
Detection of Eyes Position Based on Electrooculography Signal Analysis
Authors: Robert Czabański, Tomasz Przybyła, Tomasz Pander
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this paper we reported initial work at development of a human-machine interface system for people with severe disabilities based on measurement of electrooculography signal (EOG). We proposed a system for detecting and predicting eyes position using EOG. We applied a zero-order Takagi-Sugeno-Kang model with modified inference procedure for this task. To calculate values of parameters of fuzzy system we used results of EOG signal segmentation. Experimentation shows the usefulness of the presented method for improving functionality of interface systems that can assist people with limited upper body mobility.
Implementation Relations for the Distributed Test Architecture
Authors: Robert M. Hierons, Mercedes G. Merayo, Manuel Núñez
Venue: Proceedings of the 20th IFIP TC 6/WG 6.1 international conference on Testing of Software and Communicating Systems: 8th International Workshop (2008)

Some systems interact with their environment at a number of physically distributed interfaces called ports. When testing such a system under test (SUT) it is normal to place a local tester at each port and the local testers form a local test case. If the local testers cannot interact with one another and there is no global clock then we are testing in the distributed test architecture. In this paper we explore the effect of the distributed test architecture when testing an SUT against an input output transition system, adapting the ioco implementation relation to this situation. In addition, we define what it means for a local test case to be deterministic, showing that we cannot always implement a deterministic global test case as a deterministic local test case. Finally, we show how a global test case can be mapped to a local test case.
Expected Π-Adic Security Measures of Sequences
Authors: Andrew Klapper
Venue: Proceedings of the 5th international conference on Sequences and Their Applications (2008)

Associated with a class of AFSRs based on a ring R and &Pi; &iquest; R, there is a security measure, the &Pi; -adic complexity of a sequence. To understand the normal behavior of &Pi; -adic complexity we can find the average &Pi; -adic complexity, averaged over all sequences of a given period. This has been done previously for linear and p-adic complexity. In this paper we show that when &Pi; 2 = 2, the average &Pi; -adic complexity of period n sequences is n &Pi; O(log(n)).
An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts
Authors: Ruey-Shiang Guh, Yeou-Ren Shiue
Venue: Computers and Industrial Engineering (2008)

With modern data acquisition system and on-line computer used during production, it is now common to monitor several correlated quality variables simultaneously. Various multivariate control charts (e.g., Hotelling's T^2, multivariate cumulative sum, and multivariate exponentially weighted moving average charts) have been designed for detecting mean shifts. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal. Using decision tree learning techniques, this work proposes a straightforward and effective model to detect the mean shifts in multivariate control charts. Experimental results using simulation show that the proposed model cannot only efficiently detect the mean shifts but also accurately identify the variables that have deviated from their original means. The shift direction of each of the deviated variables can also be determined in the meantime. The experimental results also indicate that the learning speed of the proposed decision tree learning-based model is much faster (25 times in this research) than that of a neural network-based model (another machine learning-based approach) for detecting mean shifts in multivariate control charts. The feature of fast learning makes the proposed DT learning-based model more adaptable to a dynamic process-monitoring scenario, in which constant model re-designing and re-learning is required. A bivariate case of the proposed multivariate model is presented. A demonstrative application is provided to illustrate the usage of the proposed decision tree learning-based approach to multivariate quality control.
Formal Functional Verification of Device Drivers
Authors: Eyad Alkassar, Mark A. Hillebrand
Venue: Proceedings of the 2nd international conference on Verified Software: Theories, Tools, Experiments (2008)

We report on the formal functional verification of a simple device driver for an ATAPI hard disk in Isabelle/HOL. The proof is based on a functional model of the hard disk, which has been integrated into the instruction set architecture of a verified RISC processor as one of several memory-mapped devices. The result is an interleaved computational model, in which the devices and the processor take turns in execution. Even in this concurrent context, the verification can be kept largely sequential and modular with respect to the other devices. This is made possible by sound reordering of computation traces, given that devices do not interfere with each other and the driver monopolizes the hard disk. To the best of our knowledge, this paper presents the first formal functional verification of a device driver against a realistic device and system model.
Real-Time People Counting Using Multiple Lines
Authors: Javier Barandiaran, Berta Murguia, Fernando Boto
Venue: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services (2008)

A novel real-time people counting system is presented in this paper. Using a single overhead mounted camera, the system counts the number of people going in and out of an observed area. Counting is performed by analyzing an image zone composed by a set of virtual counting lines. The system runs on a commercial PC, does not need a special background and is easily adjustable to different camera height requirements. We have tested the performance of the system, achieving a correct people counting rate of 95%.
Learning from time series in the presence of noise: unsupervised and semi-supervised approaches
Authors: Eamonn Keogh
Venue:  (2008)

Characteristic for most real-world data mining tasks are the availability of a large number of unlabeled examples and the large rates of noise obliterating the similarity patterns. We study datasets with such characteristics collected across a wealth of domains - web search queries, celestial systems, anthropological artifacts, surveillance footages, etc. Diverse as they are, data from these domains turn out to have intuitive time series representation. As a major theme herein we advocate the belief that the time series representation is versatile enough to allow for accurate learning in the presence of noise, and when there are few or no labeled examples available. This is further achieved without compromising on the efficiency and the scalability of the problems at hand. For certain applications the noisy patterns themselves can be of particular interest - anomalous trajectories, surprising web queries, erroneously recorded light-curves - identifying them, and drawing the domain expert's attention to them, often leads to unexpected discoveries. The early chapters of the thesis introduce the time series discords as a notion of "interesting" outliers in the high dimensional representation space. We develop an effective and highly efficient discord detection algorithm, which used in a parallel fashion can handle hundreds of millions of examples in only a few hours. We then turn our attention to the question of how to decouple the noise from the structured signal within the data. The problem can be especially hard when the time series are embedded within complex, non-convex topological spaces. Local manifold reconstruction techniques, such as Isomap and Locally Linear Embedding, easily fail in the realistic settings of noise. An extension of the Isomap algorithm is thus derived, that remedies the effect of noise and learns more accurately the data embedding subspaces. As a more principled approach, we further develop a dual treatment of the time series manifold reconstruction problem. It computes a global density estimate of the data, which is then regularized locally through the help of multiple factor analyzer models. This results into smoothly reconstructed time series manifolds, unbiased by the effect of the noisy examples. Cast within a semi-supervised framework, our method achieves similar accuracy and several orders of magnitude speed-up over the state of the art transductive learning approaches.
Enclosing Machine Learning for Class Description
Authors: Xunkai Wei, Johan Löfberg, Yue Feng, Yinghong Li, Yufei Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A novel machine learning paradigm, i.e. enclosing machine learning based on regular geometric shapes was proposed. It adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, and box) or their unions and so on to obtain one class description model and thus imitate the human "Cognizing" process. A point detection and assignment algorithm based on the one class description model was presented to imitate the human "Recognizing" process. To illustrate the concept and algorithm, a minimum volume enclosing ellipsoid (MVEE) strategy for enclosing machine learning was investigated in detail. A regularized minimum volume enclosing ellipsoid problem and dual form were presented due to probable existence of zero eigenvalues in regular MVEE problem. To solve the high dimensional one class description problem, the MVEE in kernel defined feature space was presented. A corresponding dual form and kernelized Mahalanobis distance formula was presented. We investigated the performance of the enclosing learning machine via benchmark datasets and compared with support vector machines (SVM).
Topic Segmentation in a Media Watch System
Authors: Rui Amaral, Isabel Trancoso
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

This paper describes our on-going work on the topic segmentation module of a media watch system. The current version explores not only the typical structure of a broadcast news show, but also its contents, which are automatically produced by the speech recognition module, and the topic indexation module. The performance of the automatic topic segmentation module was compared with the manual segmentation done by a professional media watch company, yielding quite satisfactory results.
Plenary lecture IV: on the sound propagation in the open air
Authors: Veturia Chiroiu
Venue: Proceedings of the 9th WSEAS International Conference on Acoustics & Music: Theory & Applications (2008)

This contribution is planned to provide the application of the soliton theory to understand the sound propagation in the open air. The sound propagation in the atmosphere is more complicated than the theory of geometrical spreading above a flat hard ground. When sound propagates, it is attenuated with increasing distance between source and receiver, and the sound characteristics depend on time and the distance from the source. Grounds may not be flat and also acoustically soft, the wind and temperature refract sound either upwards or downwards at the ground, leading to complex reflection coefficients and the multiple reflections at the ground. Atmospheric turbulence causes fluctuations and scatters sound into acoustical shadow zones. The methodology to study the sound evolution equation is the soliton theory. The sound is regarded as an entity, a quasi-particle, characterized by a proper propagation mechanism which conserves its character and interacts with the ground properties and micrometeorological factors. The sound propagation theory is faced with the unexpected appearance of chaos or order. Within this framework the soliton plays the role of order. The results obtained in the linear theory of sound motion, by ignoring the nonlinear parts, are most frequently too far from reality to be useful. The linearization misses an important phenomenon, solitons, which are waves, which maintain their identity indefinitely just when we most expect that dispersion effects will lead to their disappearance. The solutions regarding the attenuation due to atmospheric absorption, the decrease in sound pressure level with different factors, are represented by the revolution ruled Tzitzeica surfaces. The capability of the Bäcklund transformation to provide an integrable discretization of the characteristic equations associated to the sound propagation, are considered for modeling the sound rays during downwind and upwind propagation.
Functional Learning of Kernels for Information Fusion Purposes
Authors: Alberto Muñoz, Javier González
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

When there are several sources of information available in pattern recognition problems, the task of combining them is most interesting. In the context of kernel methods it means to design a single kernel function that collects all the relevant information of each kernel for the classification task at hand. The problem is then solved by training a Support Vector Machine (SVM) on the resulting kernel. Here we propose a consistent method to produce kernel functions from kernel matrices created by any given kernel combination technique. Once this fusion kernel function is available, it will be possible to evaluate the kernel at any data point. The performance of the proposed fusion Kernel is illustrated on several classification and visualization tasks.
Optimizing Service Distributions Using a Genetic Algorithm
Authors: Kresimir Jurasovic, Mario Kusek
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

This paper deals with optimizing the distribution of services performed by agents in a Mobile Agent Network (MAN). The MAN model consists of agents, capable of performing remote software management services, and of various network elements and nodes used by agents during service execution. In order to verify various service distributions a Mobile Agent Network Simulator was developed. This simulator allows the user to define the services which need to be performed by the agents and describe the physical network used in the process. In this paper, an approach using a genetic algorithm which optimizes the distribution of services with the objective to reduce the network load and total execution time is presented.
CorrefSum: Referencial Cohesion Recovery in Extractive Summaries
Authors: Patrícia Nunes Gonçalves, Renata Vieira, Lucia Helena Rino
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

A common problem in extractive summaries is the occurrence of referential expressions which are of difficult interpretation. In this paper we propose and evaluate a system for summary post-edition, which aims at replacing referential expressions, trying to avoid problems of broken referential linkage. To propose expressions that best represent the evoked entity, the system uses knowledge about coreference chains.