ppxscal/embedding_network huggingface dataset:
Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation
Authors: Germán Sánchez, Núria Agell, Juan Carlos Aguado, Mónica Sánchez, Francesc Prats
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

The use of unsupervised fuzzy learning methods produces a large number of alternative classifications. This paper presents and analyzes a series of criteria to select the most suitable of these classifications. Segmenting the clients' portfolio is important in terms of decision-making in marketing because it allows for the discovery of hidden profiles which would not be detected with other methods and it establishes different strategies for each defined segment. In the case included, classifications have been obtained via the LAMDA algorithm. The use of these criteria reduces remarkably the search space and offers a tool to marketing experts in their decision-making.
Nonparametric Time-Varying Phasor Estimation Using Neural Networks
Authors: Jaco Jordaan, Anton Wyk, Ben Wyk
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

A new approach to nonparametric signal modelling techniques for tracking time-varying phasors of voltage and current in power systems is investigated. A first order polynomial is used to approximate these signals locally on a sliding window of fixed length. Non-quadratic methods to fit the linear function to the data, give superior performance over least squares methods in terms of accuracy. But these non-quadratic methods are iterative procedures and are much slower than the least squares method. A neural network is then used to model the non-quadratic methods. Once the neural network is trained, it is much faster than the least squares and the non-quadratic methods. The paper concludes with the presentation of the representative testing results.
Resolving Occlusion Method of Virtual Object in Simulation Using Snake and Picking Algorithm
Authors: Jeonghee Cha, Gyeyoung Kim, Hyungil Choi
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

For realistic simulation, it is essential to register the two worlds, calculate the occlusion realm between the real world and the virtual object, and determine the location of the virtual object based on the calculation. However, if the constructed map is not accurate or the density is not sufficient to estimate the occlusion boundary, it is very difficult to determine the occlusion realm. In order to solve this problem, this paper proposes a new method for calculating the occlusion realm using the snake and picking algorithm. First, the wireframe generated by the CCD image and DEM was mapped using the visual clues to acquire 3D information in the experimental realm, and the 3D information was calculated at the point where occlusion problem for a moving target. The validity of the proposed approach under the environment in which partial occlusion occurs has been provided by an experiment.
Collaborative defense against zero-day and polymorphic worms: detection, response and an evaluation framework
Authors: Karl N. Levitt
Venue:  (2007)

Computer worms present a grave concern to the common man, and a challenging problem to the computer security community. Worms' abilities have precluded human intervention. East worms can be too fast to respond to. Slow worms can be too slow to be noticed. Zero-day and polymorphic worms can look like ordinary traffic to evoke any suspicion until they cause large scale destruction. This demands not just automated response but automated and intelligent response. This dissertation presents such an automated and intelligent means of detecting and responding to zero-day worms that could possibly be polymorphic in a signature independent fashion. Worms are detected cooperatively using a novel distributed application of the long-established Sequential Hypothesis Testing technique. The technique developed here builds a distributed worm detector of any desirable fidelity from unreliable anomaly detection systems. Tracking anomalies instead of signatures enables detection of zero-day and polymorphic worms. Cost-effective responses in the face of uncertainty about worms are selected automatically using Dynamic Programming. Responses are selected based on the likelihood of a current worm attack, and the relative costs of infection and responses, while minimizing the operating cost over a period of time. This technique uses information about anomalous events, potentially due to a worm, observed by cooperating peers to choose optimal actions for local implementation. In addition to developing the above techniques, this dissertation also presents a generic testing framework based on the Emulab network testbed to evaluate these and other such worm defense models, and provides a detailed survey of the research done so far in worm defense.
On Properly Using Technologies to Make E-Learning Effective
Authors: Won Kim, Ok-Ran Jeong
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

E-learning is learning without the presence of a human instructor in class. Advances in technologies have made e-learning possible. E-learning can complement the traditional in-class learning. However, e-learning contents are often boring, or unclear, or detract from learning. If e-learning can be much more effective than today, technologies must be used properly in creating the learning contents, without losing the focus on learning. In this paper, we examine the different types of technologies available, and how they may be used to make e-learning effective.
Bounded Asynchrony: Concurrency for Modeling Cell-Cell Interactions
Authors: Jasmin Fisher, Thomas A. Henzinger, Maria Mateescu, Nir Piterman
Venue: Proceedings of the 1st international workshop on Formal Methods in Systems Biology (2008)

We introduce bounded asynchrony, a notion of concurrency tailored to the modeling of biological cell-cell interactions. Bounded asynchrony is the result of a scheduler that bounds the number of steps that one process gets ahead of other processes; this allows the components of a system to move independently while keeping them coupled. Bounded asynchrony accurately reproduces the experimental observations made about certain cell-cell interactions: its constrained nondeterminism captures the variability observed in cells that, although equally potent, assume distinct fates. Real-life cells are not "scheduled", but we show that distributed real-time behavior can lead to component interactions that are observationally equivalent to bounded asynchrony; this provides a possible mechanistic explanation for the phenomena observed during cell fate specification.We use model checking to determine cell fates. The nondeterminism of bounded asynchrony causes state explosion during model checking, but partial-order methods are not directly applicable. We present a new algorithm that reduces the number of states that need to be explored: our optimization takes advantage of the bounded-asynchronous progress and the spatially local interactions of components that model cells. We compare our own communication-based reduction with partial-order reduction (on a restricted form of bounded asynchrony) and experiments illustrate that our algorithm leads to significant savings.
A Template-Based Shape Representation Technique
Authors: Yasser Ebrahim, Maher Ahmed, Siu-Cheung Chau, Wegdan Abdelsalam
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper we present a novel approach to shape representation based on correlating a set of object Regions of Interest (RoI) with a set of shape templates. The resultant correlations are the shape features used to build a Template-based Shape Feature Vector (TSFV) that represents the shape of the object. For each class of objects, a set of Main Shape Features (MSFs) is determined so that only the most descriptive features are used when comparing shapes. The proposed technique is tested on two benchmark databases, Kimia-99 and Kimia-216 and is shown to produce competitive results.
Brief paper: Control for discrete singular hybrid systems
Authors: Yuanqing Xia, Jinhui Zhang, El-Kebir Boukas
Venue: Automatica (Journal of IFAC) (2008)

The problems of stability, state feedback control and static output feedback control for a class of discrete-time singular hybrid systems are investigated in this paper. A new sufficient and necessary condition for a class of discrete-time singular hybrid systems to be regular, causal and stochastically stable is proposed in terms of a set of coupled strict linear matrix inequalities (LMIs). Sufficient conditions are proposed for the existence of state feedback controller and static output feedback controller in terms of a set of coupled strict LMIs, respectively. Finally, two illustrative examples are provided to demonstrate the applicability of the proposed approach.
An Aggregation of Agents, Roles and Coalition Formation to Support Collaborative and Dynamic Organizations
Authors: Nora Houari, Behrouz H. Far
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

Capturing human resources, their capabilities and dynamics can greatly contribute to an efficient and effective organization mission goals accomplishment. In practice however, this is often done in an ad-hoc manner leading to a big gap between the organizational specification model (at the design time) and its instance model (during the life cycle). In this paper we present a generic organizational model that captures the human resources, their operational and strategic capabilities, and adaptive coalition formation. The key elements of the model are capabilities, roles, agents and coalitions. We show how the resulting model can be used for role functionalities and performance control, as well as for capturing the knowledge in context-aware applications which is often present in an implicit manner.
Dilatation and Pattern Formation of Cells in Internally Heated Convection
Authors: Y. Tasaka, K. Yonekura_affanb, Y. Takeda, T. Yanagisawa
Venue: Journal of Visualization (2008)

The dilatation and the pattern formation of convection cells in natural convection driven by internal heat generation, first investigated during the 1970s, are reexamined in this paper using an improved experimental apparatus in order to reduce uncertainties as much as possible. The convective motion in the fluid layer was visualized using reflecting particles (Kalliroscope). Cell dilatation was confirmed in the improved experimental apparatus and then investigated quantitatively by extracting the pattern wavenumber from Fourier analysis of the images recorded by a digital camera. The pattern wavenumber was found to decrease with increasing Rayleigh number. We compare our results with earlier investigations and discuss the influence of the thermal boundary condition at the bottom of the fluid layer on the variation of the wavenumber. Two different structures were observed in the same fluid layer at a relatively high Rayleigh number; an additional cell appears in the original cell (Double cell structure) and the descending flow region around the center of the cell expands laterally like a spoke of wheels (Spoke-like structure). Factors for the formation of such complex structures are discussed.
Monte Carlo geometry optimization of Si$_n$ (n &les; 71) clusters
Authors: Nazım Dugan, Şakir Erkoç
Venue: Journal of Computational Methods in Sciences and Engineering (2007)

Optimum geometries of silicon clusters up to 71 atoms have been found by a recently developed Monte Carlo based global optimization method. Structural properties of these clusters have been investigated and the results have been compared with available results obtained by other methods. Radial distribution of atoms of Si$_{71}$ have been compared with the silicon crystal structure.
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Authors: Tiago A. Ferreira, Germano C. Vasconcelos, Paulo J. Adeodato
Venue: Neural Processing Letters (2008)

The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method for time series prediction that performs an evolutionary search for the minimum number of dimensions necessary to represent the underlying information that generates the time series. The methodology proposed is inspired in Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network combined with a modified genetic algorithm. Initially, the TAEF method finds the best fitted model to forecast the series and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of some series. An experimental investigation conducted with relevant time series show the robustness of the method through a comparison, according to several performance measures, to previous results found in the literature and those obtained with more traditional methods.
Information Geometry and Information Theory in Machine Learning
Authors: Kazushi Ikeda, Kazunori Iwata
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Information geometry is a general framework of Riemannian manifolds with dual affine connections. Some manifolds (e.g. the manifold of an exponential family) have natural connections (e.g. e- and m-connections) with which the manifold is dually-flat. Conversely, a dually-flat structure can be introduced into a manifold from a potential function. This paper shows the case of quasi-additive algorithms as an example.Information theory is another important tool in machine learning. Many of its applications consider information-theoretic quantities such as the entropy and the mutual information, but few fully recognize the underlying essence of them. The asymptotic equipartition property is one of the essence in information theory.This paper gives an example of the property in a Markov decision process and shows how it is related to return maximization in reinforcement learning.
Improving Temporal-Awareness of WS-Agreement
Authors: C. Müller, O. Martín-Díaz, A. Ruiz-Cortés, M. Resinas, P. Fernández
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

WS-Agreement (WS-Ag) is a proposed recommendation of the Open Grid Forum that provides a schema to describe SLAs and a protocol to create them based on a mechanism of templates. However, although it identifies the necessity of specifying temporal-aware agreement terms (e.g. the response time is 30 ms from 8:00h to 17:00h and 15 ms from 17:00h to 8:00h), to the best of our knowledge, there are no existing proposals that deal with that necessity. We propose an extension that gives WS-Ag support to temporality. This allows describing expressive validity periods such as those composed by several periodic or non-periodic intervals and it applies not only to the agreement terms themselves but also to other parts of WS-Ag such as creation constraints and preferences about the service properties. In addition, in this paper we propose a preference XML schema to describe preferences over any set of service properties using any kind of utility function. In further research we will study a concrete specification for those utility functions.
A Secure DRM Framework for User's Domain and Key Management
Authors: Jinheung Lee, Sanggon Lee, Sanguk Shin
Venue: Proceedings of the 4th international conference on Autonomic and Trusted Computing (2007)

Digital rights management(DRM) systems are used to control the use and distribution of copyrighted content. OMA specification creates devices collection of domain, and the use of contents is free in this domain. Domains are created and managed directly by the RI(rights issuer) that issues rights to the domain. In this paper, we propose a new rights object acquisition protocol(ROAP) for DRM and a efficient key distribution protocol. The proposed ROAP provides billing functionality for purchasing rights via network operator which was not considered in OMA DRM 2.0 specifications.
Using Intelligent Optimization Methods to Improve the Group Method of Data Handling in Time Series Prediction
Authors: Maysam Abbod, Karishma Deshpande
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm.
On the Facilitation of Fine-Grained Access to Distributed Healthcare Data
Authors: Mark Slaymaker, David Power, Douglas Russell, Andrew Simpson
Venue: Proceedings of the 5th VLDB workshop on Secure Data Management (2008)

As an increasing amount of healthcare-related data is captured in both clinical and research contexts, the drive to provide appropriate access to such data becomes stronger. The very nature of such data means that simplistic approaches to authorisation--be they coarse-grained or role-based--are insufficient: the needs of the domain give rise to requirements for authorisation models capable of capturing fine-grained, expressive access control policies. We describe the development of a framework for the secure sharing and aggregation of healthcare-related data, called sif (for service-oriented interoperability framework). In particular, we concentrate on the access control aspects of the system and describe its utilisation of XACML in this respect.
Structural properties of lithium clusters: A Monte Carlo application
Authors: Nazım Dugan, Şakir Erkoç
Venue: Journal of Computational Methods in Sciences and Engineering (2007)

Optimum geometries of small lithium clusters have been found by a recently developed global optimization technique using an empirical potential energy function (PEF). Some information have been given about the PEF used. Several properties of these clusters have been investigated and the results have been compared with the ab initio calculations and with the results obtained by another optimization method. Radial distribution of atoms for these clusters have been compared with the lithium crystal structure. A discussion about the reasons of the inaccuracy in the results have also been presented.
Kohonen-Swarm Algorithm for Unstructured Data in Surface Reconstruction
Authors: Fadni Bin Forkan, Siti Mariyam Hj Shamsuddin
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

This work introduces a new method for surface reconstruction based on hybrid soft computing techniques: Kohonen Network and Particle Swarm Optimization (PSO). Kohonen network learns the sample data through mapping grid that can grow. The implementation is executed by generating Kohonen mapping framework of the data subsequent to the learning process. Consequently, the learned and well-represented data become the input for surface fitting procedure, and in this study, PSO is proposed to probe the optimum fitting points on the surfaces. The proposed algorithms are applied on different types of curve and surfaces to observe its ability in reconstructing the objects while preserving the original shapes. The experimental results have shown that the proposed algorithm have succeeded in producing the reconstructed surfaces with minimum errors generated.
Force Feedback Virtual Painting on Real Objects: A Paradigm of Augmented Reality Haptics
Authors: Benjamin Bayart, Jean-Yves Didier, Abderrahmane Kheddar
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

This paper presents a paradigm of augmented reality haptic through an application enabling the interaction on real objects with a virtual tool. In order to interact within the real world, a real haptic probe is used so that the user feels the interaction. Furthermore, through the use of a visual partial reality removal process and a camera placed on the real scene, the real tool is visually hidden in the visual feedback and replaced by the virtual tool. Since, the real and virtual probes do not necessarily match, a model of the virtual tool is used to adjust and tune the haptic feedback, while at the same time the virtual tool is visually rendered according to the real measured forces by the haptic probe. Eventually, proposing a mixed painting application, the painting, applied on the real object, i.e. when the user comes in contact with this latter, is visually displayed such that its form is computed from the virtual tool geometry while its size and intensity from the real measured forces.
Using Intelligent Feedback to Improve Sourcing and Integration in Students' Essays
Authors: M. Anne Britt, Peter Wiemer-Hastings, Aaron A. Larson, Charles A. Perfetti
Venue: International Journal of Artificial Intelligence in Education (2004)

Learning and reasoning from multiple documents requires students to employ the skills of sourcing (i.e., attending to and citing sources) and information integration (i.e., making connections among content from different sources). Sourcer's Apprentice Intelligent Feedback mechanism (SAIF) is a tool for providing students with automatic and immediate feedback on their use of these skills during the writing process. SAIF uses Latent Semantic Analysis (LSA), a string-matching technique and a pattern-matching algorithm to identify problems in students' essays. These problems include plagiarism, uncited quotation, lack of citations, and limited content integration. SAIF provides feedback and constructs examples to demonstrate explicit citations to help students improve their essays. In addition to describing SAIF, we also present the results of two experiments. In the first experiment, SAIF was found to detect source identification and integration problems in student essays at a comparable level to human raters. The second experiment tested the effectiveness of SAIF in helping students write better essays. Students given SAIF feedback included more explicit citations in their essays than students given sourcing-reminder instructions or a simple prompt to revise.
Probabilistic CEGAR
Authors: Holger Hermanns, Björn Wachter, Lijun Zhang
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

Counterexample-guided abstraction refinement (CEGAR) has been en vogue for the automatic verification of very large systems in the past years. When trying to apply CEGAR to the verification of probabilistic systems, various foundational questions arise. This paper explores them in the context of predicate abstraction.
Brief paper: Command-induced vibration analysis using input shaping principles
Authors: Khalid L. Sorensen, William E. Singhose
Venue: Automatica (Journal of IFAC) (2008)

Input shaping is a well-established technique used for reducing the vibratory response of dynamic systems. Analytical tools are available for systems utilizing input shaping. These tools aid in performance analysis by providing intuitive and computationally simple methods for determining key system attributes, such as the residual vibration in response to a command. This paper describes methods whereby arbitrary reference commands may be interpreted as input-shaped commands. This capability allows input shaping analysis tools to be used on systems without input shapers. Experimental results obtained from an industrial 10-ton bridge crane validate the theoretical developments.