ppxscal/embedding_network huggingface dataset:
Improved Production of Competitive Learning Rules with an Additional Term for Vector Quantization
Authors: Enrique Mérida-Casermeiro, Domingo López-Rodríguez, Gloria Galán-Marín, Juan M. Ortiz-De-Lazcano-Lobato
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

In this work, a general framework for developing learning rules with an added term (perturbation term) is presented. Many learning rules commonly cited in the specialized literature can be derived from this general framework. This framework allows us to introduce some knowledge about vector quantization (as an optimization problem) in the distortion function in order to derive a new learning rule that uses that information to avoid certain local minima of the distortion function, leading to better performance than classical models. Computational experiments in image compression show that our proposed rule, derived from this general framework, can achieve better results than simple competitive learning and other models, with codebooks of less distortion.
Wavelet-Packet Identification of Dynamic Systems with Coloured Measurement Noise
Authors: Henrique Mohallem Paiva, Roberto Kawakami Harrop Galvão
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

This paper analyses the effect of coloured noise on a recently proposed technique for linear system identification in frequency subbands using wavelet packets. For this purpose, a simulation study involving the longitudinal dynamics of a flexible aircraft model is presented. The results reveal that the wavelet-packet identification outcome is robust with respect to changes in the spectral noise features. In particular, the identified frequency response is effectively smoothed in regions with poor signal-to-noise ratio. Finally, the results are favourably compared, in terms of resonance peak identification, with those obtained by standard time-domain identification methods.
Oil Price Forecasting with an EMD-Based Multiscale Neural Network Learning Paradigm
Authors: Lean Yu, Kin Keung Lai, Shouyang Wang, Kaijian He
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic mode components (IMCs) with a range of frequency scales. Then the internal correlation structures of different IMCs are explored by neural network model. With the neural network weights, some important IMCs are selected as final neural network inputs and some unimportant IMCs that are of little use in the mapping of input to output are discarded. Finally, the selected IMCs are input into another neural network model for prediction purpose. For verification, the proposed multiscale neural network learning paradigm is applied to a typical crude oil price -- West Texas Intermediate (WTI) crude oil spot price prediction.
Models and Tools for Mulan Applications
Authors: Lawrence Cabac, Till Dörges, Michael Duvigneau, Christine Reese, Matthias Wester-Ebbinghaus
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

In this work we describe the development process of multi-agent application design and implementation with Mulan. Our approach can be characterized as model driven development by using models in all stages and levels of abstraction regarding design, implementation and documentation. Both, standard methods from software development as well as customized ones are used to satisfy the needs of multi-agent system development.
Computational Model for Aircraft's Takeoffs Pattern Recognition
Authors: Arturo Rojo Ruiz, Luis P. Sánchez Fernandez, Edgardo Felipe-Riverón, Sergio Suárez Guerra
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

This paper presents a novel computational multimodal model designed for pattern recognition of aircrafts' noise in real environments; with an 88.5% of effectiveness, it considers 13 different categories of aircrafts. This method includes measurements of signals of the noise produced during the takeoff at 25,000 samples per second and with a resolution of 24 bits, an spectral analysis made by means of an autoregressive model, an octave analysis, a normalization method created specifically for this work and two feed-forward neural networks. All the signals used for the design and evaluation of the results were obtained by means of field measurements.
Target Clustering of Genes by Normal Mixture Model in Microarray Analysis
Authors: Seung-Gu Kim
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

To cluster genes in microarray gene expression data experimented over known classes (conditions), this paper provides a model-based gene cluster method. This method takes the normality for component densities from so called splitting and averaging of class data where the number of components is given, and designs component means by general linear model subject to various linear restrictions in order to induce the targeted clusters that an analyst previously want to discover.
Web Application Construction by Group Work and Practice
Authors: Kouji Yoshida, Isao Miyaji, Hiroshi Ichimura, Kunihiro Yamada
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In recent years, it is important that information technology (IT) students have the experience of developing software development in a group similar to that of large-scale software development. Many students are not only weak in communication, but also are not good at cooperation and collaboration when designing a program. Therefore, we had IT students practice group work in a seminar course and analyzed its effects and problems.Generally, students are good at the acquisition of knowledge that is constant in distance learning, but find that it is difficult to finish a program by oneself. In distance learning, it is very important to employ mutual teaching and regular communication between students. When there is active discussion, cooperation, and differing opinions among students, new software ideas can more effectively take shape. On the other hand, group collaboration can also limit the freedom of a student's expression of individual ideas. We report that it was very important to effectively balance student collaboration with the individual's freedom to make their own decisions.
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
Authors: Luiz A. Celiberto,  Jr., Carlos H. Ribeiro, Anna H. Costa, Reinaldo A. Bianchi
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning algorithm, the Heuristically Accelerated Q---Learning (HAQL). This algorithm allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q---Learning. A heuristic function that influences the choice of the actions characterizes the HAQL algorithm. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results show that even very simple heuristics enhances significantly the performance of the agents.
Scaling Most Probable World Computations in Probabilistic Logic Programs
Authors: Gerardo I. Simari, Maria Vanina Martinez, Amy Sliva, V. S. Subrahmanian
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

The "Most Probable World" (MPW) problem in probabilistic logic programming (PLPs) is that of finding a possible world with the highest probability. Past work has shown that this problem is computationally intractable and involves solving exponentially many linear programs, each of which is of exponential size. In this paper, we study what happens when the user focuses his interest on a set of atoms in such a PLP. We show that we can significantly reduce the number of worlds to be considered by defining a "reduced" linear program whose solution is in one-one correspondence with the exact solution to the MPW problem. However, the problem is still intractable. We develop a Monte Carlo sampling approach that enables us to build a quick approximation of the reduced linear program that allows us to estimate (inexactly) the exact solution to the MPW problem. We show experimentally that our approach works well in practice, scaling well to problems where the exact solution is intractable to compute.
Some modification of Newton's method by the method of undetermined coefficients
Authors: Changbum Chun, Beny Neta
Venue: Computers Mathematics with Applications (2008)

In this paper, we construct some modifications of Newton's method for solving nonlinear equations, which is based on the method of undetermined coefficients. It is shown by way of illustration that the method of undetermined coefficients is a promising tool for developing new methods, and reveals its wide applicability by obtaining some existing methods as special cases. Two new sixth-order methods are developed. Numerical examples are given to support that the methods thus obtained can compete with other iterative methods.
Model-Driven Performability Analysis of Composite Web Services
Authors: Paolo Bocciarelli, Andrea D'Ambrogio
Venue: Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks (2008)

Web services are the building blocks of the emerging computing paradigm based on service-oriented architectures (SOAs). A web service is a self-describing, open component that supports rapid composition of distributed applications. In a SOA context, service providers are strategically interested both to predict and describe the QoS of the offered services. This paper introduces a model-driven approach to automatically predict and describe the QoS of composite web services specified by use of the Business Process Execution Language for Web Services. The paper is founded on a lightweight QoS-oriented extension of the WSDL and specifically addresses the QoS in terms of the performability attribute, which defines a combined measure of performance and reliability. The proposed approach is illustrated by use of an example application that shows how the performability analysis may lead to predictions that do not correspond to those obtained by approaches that only consider the performance attribute.
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Authors: Volker Nannen, Selmar K. Smit, Agoston E. Eiben
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered--parent selection, survivor selection, recombination and mutation--and for each component we study the impact of choosing the right operator, and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes, and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.
Administrative supervision and information relationships
Authors: Victor Bekkers, Vincent Homburg
Venue: Information Polity (2002)

In many countries, administrative supervision has grown dramatically in recent years. Administrative supervision is a form of interaction between policy makers and policy executors, aimed at improving political accountability. In this paper, the role of information and information relationships between policy-making bodies, executive institutions and administrative supervisors is explored. We identify three roles of administrative supervisors: a classical (cop) role, a modern (coach) role, and a networking (director) role. Each role has requirements with respect to the information relationship, particularly in the relationship between the supervisory authority and the executive institution. In this paper, we analyze the sometimes contradictory roles of administrative supervisors and the implications for information relationships, and we indicate the consequences for practice.
EANT+KALMAN: An Efficient Reinforcement Learning Method for Continuous State Partially Observable Domains
Authors: Yohannes Kassahun, Jose Gea, Jan Hendrik Metzen, Mark Edgington, Frank Kirchner
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

In this contribution we present an extension of a neuroevolutionary method called Evolutionary Acquisition of Neural Topologies (EANT) [11] that allows the evolution of solutions taking the form of a POMDP agent (Partially Observable Markov Decision Process) [8]. The solution we propose involves cascading a Kalman filter [10] (state estimator) and a feed-forward neural network. The extension (EANT+KALMAN) has been tested on the double pole balancing without velocity benchmark, achieving significantly better results than the to date published results of other algorithms.
"SketSha" --- The Sketch Power to Support Collaborative Design
Authors: Catherine Elsen, Pierre Leclercq
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

This paper presents a new supporting tool for distant collaborative design, named SketSha. This prototype supports the early stages of design and more particularly the initial and crucial step of free-hand sketching. SketSha and its particular interface, named the virtual desktop, aim to keep from Front-To-Front collaborative work all the benefits and to appoint to them some of the nowadays IT facilities, in order to manage in a realistic and efficient way a long distance collaboration and to effectively serve the designers' needs. Our hope is to re-introduce the social aspects and group interactions, crucial for real system efficacy and adaptation to the business world. The paper presents the fundamental assumptions made to implement SketSha, that are questioned and analyzed through a real-size experimentation featuring 30 architecture and building engineering students, working together in real-time in different locations (Belgium and France). This experiment and the necessary survey open up interesting fields of investigations, such as the relevance of the sketch stage and the proposed device to support distant collaborative design in architecture and the benefit it represents for students, in a pedagogical point of view, to merge the IT aspects and the design studio. The methodology and the replicability are analyzed to increase the level and quality of our students' formation and, finally, a critic of SketSha constitutes a benefit for the developing teams.
Weighted Conjunctive and Disjunctive Aggregation of Possibilistic Truth Values
Authors: Tom Matthé, Guy Tré, Axel Hallez
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

In this paper, weighted aggregation of extended possibilistic truth values will be presented. The impact of the weights on both conjunctive and disjunctive aggregation will be handled, and special care will be given to the case where both conjunctive and disjunctive aggregation are mixed together. The problems that can arise will be illustrated and solutions will be proposed.
Privacy-Preserving Set Union
Authors: Keith Frikken
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

Recently there has been a significant amount of work on privacy-preserving set operations, including: set intersection [14,6,21,9], testing set disjointness [17], multi-set operations [18], and set union [16,1,18] . In this paper, we introduce novel protocols for privacy- preserving set union in the malicious adversary model. More specifically, each participant inputs a set of values, and at the end of the protocol, each participant learns the items that are in at least one participant's set without learning the frequency of the items or which participant(s) contributed specific items. To our knowledge our protocol is the most efficient privacy-preserving set union protocol for the malicious adversary model to date.
Towards the Implementation of Reliable Data Transmission for 802.15.4-Based Wireless Sensor Networks
Authors: Taeshik Shon, Hyohyun Choi
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

Reliable data transmission in a wireless sensor network is one of the most significant factors in order to guarantee dependable and efficient sensor data delivery. As sensor network applications diversify and increase, the transmission reliability of sensor data is of uppermost importance. In this paper, we propose a hybrid hop-by-hop reliable approach based on IEEE 802.15.4, which has adaptive link control and enhanced hop-by-hop reliability. The adaptive link control scheme considers the packet's application type and the destination's link status. The proposed hybrid hop-by-hop approach selects an optimized MAC transmission state of related parameters, followed by an additional operation of finding alternative paths for a reliable transmission. In addition, a nodal caching and one hop acknowledgement schemes are used for enhancing hop-by-hop reliability further. We evaluated the proposed approach using NS-2 and C code simulation. Compared with the IEEE 802.15.4 MAC scheme, the simulation results show that the proposed approach is much more robust and reliable in the large number of hops and highly error-prone situation. Finally, we showed our own sensor H/W platform and analysis tool.
Quality Measures in Uncertain Data Management
Authors: Ander Keijzer, Maurice Keulen
Venue: Proceedings of the 1st international conference on Scalable Uncertainty Management (2007)

Many applications deal with data that is uncertain. Some examples are applications dealing with sensor information, data integration applications and healthcare applications. Instead of these applications having to deal with the uncertainty, it should be the responsibility of the DBMS to manage all data including uncertain data. Several projects do research on this topic. In this paper, we introduce four measures to be used to assess and compare important characteristics of data and systems: uncertainty density, answer decisiveness and adapted precision and recall measures.
The benefits and drawbacks of using electronic identities
Authors: Geraint Price
Venue: Information Security Tech. Report (2008)

In this article we carry out a critical analysis of the benefits and drawbacks which are likely when we include electronic data to hold, validate and process the information used to generate and manage an identity. In addition, we consider the potential knock-on impact of this for the transactions which rely on this electronic identity information.
Vehicle Detection Based on Color and Edge Information
Authors: Lei Gao, Chao Li, Ting Fang, Zhang Xiong
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

The performance of existing vehicle detection algorithms are subject to many influences of environment, such as different lighting and weather conditions, moving vehicle shadows, etc. To solve these problems, a novel vehicle detection algorithm was proposed. Different from traditional methods, which use motion features to detect vehicles, the proposed method uses a representation of red colors to find the rear-lights regions of vehicles and uses symmetry measure function to analysis the symmetry of the color distribution, and figures out the accurate position of the symmetry axis. Then, the pair-edges are defined to reconstruct the integrated vehicle edges. Finally, a simple bounding box is fit to detected vehicle regions; the result is the location and dimensions of vehicles in the video. Experimental results show that the proposed method can be accurate and robust in detecting vehicles under different weather and lighting conditions, even at night. This method can also be used in many related applications, such as self-guided vehicles and driver assistance systems.
Retinal Fundus Biometric Analysis for Personal Identifications
Authors: Vitoantonio Bevilacqua, Lucia Cariello, Donatello Columbo, Domenico Daleno, Massimiliano Dellisanti Fabiano, Marco Giannini, Giuseppe Mastronardi, Marcello Castellano
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper a biometric system for personal identification, realized through the manipulation of retinal fundus images and the detection of its bifurcation points, is described. In the image pre-processing step, a strong contrast exaltation between blood vessels and the background in retinal image is carried out; then blood vessels are extracted and next the vasculature bifurcation and crossover points are identified within squared shaped regions used to window the image. Finally the features sets are compared with a pattern recognition algorithm and a novel formulation is introduced to evaluate a similarity score and to obtain the personal identification.
Dependable Geographical Routing on Wireless Sensor Networks
Authors: Yue-Shan Chang, Ming-Tsung Hsu, Hsu-Hang Liu, Tong-Ying Juang
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

Geographic routing protocols on Wireless Sensor Network (WSN) had been researched for many years, but they did not concern with the fault problem. In this paper, we propose an approach to enhance dependability of existing geographical routing protocols to deal with the fault problem and consider the routing problem based on the fault map via query-driven models. For query-driven model, a novel algorithm, called Relay Node Selection Algorithm (RNSA) is proposed, which selects a few relay nodes as temporary destinations. Simulation shows that the success rate of data transmission originated from base station can be raised substantially and the hop count is also reduced via the selected relay nodes.
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
Authors: Jose Jesus Rubio, Wen Yu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural networks for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic systems point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. A simple simulation gives the effectiveness of the suggested algorithm.