ppxscal/embedding_network huggingface dataset:
Propagation and control of stochastic signals through universal learning networks
Authors: Kotaro Hirasawa, Shingo Mabu, Jinglu Hu
Venue: Neural Networks (2006)

The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a superset of neural networks and have been applied as a universal framework for modeling and control of non-linear large-scale complex systems. However, the ULNs cannot deal with stochastic variables. Deterministic signals can be propagated through a ULN, but the ULN does not provide any stochastic characteristics of the signals propagating through it. The proposed method named Probabilistic Universal Learning Networks (PrULNs) can process stochastic variables and can train network parameters so that the signals behave with the pre-specified stochastic properties. As examples of applications of the proposed method, control and identification of non-linear dynamic systems with noises are studied, and it is shown that the method are useful for dealing with the control and identification of the non-linear stochastic systems contaminated with noises.
Contour estimation using collaborating mobile sensors
Authors: Sumana Srinivasan, Krithi Ramamritham
Venue: Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks (2006)

A mobile wireless sensor network may be deployed to detect and track a large-scale physical phenomenon such as a pollutant spill in a lake. It may be called upon to provide a description of a contour characterized by an isoline of a specific concentration value. In this paper, we examine the problem of tracing a contour of a particular concentration within a bounded region of varying pollutant concentration using a network of mobile sensors. Since controlled movement of sensors within a given region is known to improve the overall quality of measurements by reducing sensing uncertainty, we explore various ways of guiding a set of mobile sensors optimally so as to surround and trace the contour. We formulate the contour estimation problem as a nonlinear multi-extremal optimization problem. We use accuracy and latency as performance metrics and show that in majority of the cases our proposed strategy based on collaboration of sensors delivers the best performance.
Executable Acceptance Tests for Communicating Business Requirements: Customer Perspective
Authors: Grigori Melnik, Frank Maurer, Mike Chiasson
Venue: Proceedings of the conference on AGILE 2006 (2006)

Using an experimental method, we found that customers, partnered with an IT professional, are able to use executable acceptance test (storytest)-based specifications to communicate and validate functional business requirements. However, learnability and ease of use analysis indicates that an average customer may experience difficulties learning the technique. Several additional propositions are evaluated and usage observations made.
Mechanisms for Cooperative Behaviour in Agent Institutions
Authors: Martin Purvis, Sharmila Savarimuthu, Marcos De Oliveira, Maryam Purvis
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Our approach is described for maintaining sustainable cooperative behaviour in agent-based peer-to-peer systems whose environments are organized into agent institutional societies. Instead of expelling uncooperative society members, our approach considers institutional mechanisms that attempt to encourage cooperative behaviour among society members in order to maintain satisfactory operational performance of the institution. This is accomplished by dividing the society into subgroups, each of which is managed by a special monitor agent. The subgroup monitor agents help keep track of member agent reputations and manage the associated tags for each of the agents in their subgroup. Some experimental results are presented describing how these mechanisms operate.
Multiple predictor smoothing methods for sensitivity analysis
Authors: Curtis B. Storlie, Jon C. Helton
Venue: Proceedings of the 37th conference on Winter simulation (2005)

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models (GAMs), (iii) projection pursuit regression (PP_REG), and (iv) recursive partitioning regression (RP_REG). The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or response surface regression when nonlinear relationships between model inputs and model predictions are present.
Complexity of pruning strategies for the frequency domain LMS algorithm
Authors: Vasanthan Raghavan, K. M. M. Prabhu, P. C. W. Sommen
Venue: Signal Processing (2006)

Large adaptive filters are frequently used in diverse applications such as channel equalization, interference suppression, beamforming, etc. The least mean squared (LMS) algorithm and its variants form one of the basic building blocks of adaptive systems. The frequency domain implementations of the LMS algorithm are preferred in practice since the computational burden of LMS can be reduced significantly by using the vast family of fast fourier transform (FFT) algorithms. Despite the advantage of frequency domain LMS over regular-LMS, the increasing computational complexity of the FFT-based LMS algorithms (with filter length) makes them unattractive for applications with a large number of filter taps. In this paper, FFT pruning is used to reduce the computational cost of frequency domain LMS by exploiting the structure of the LMS algorithm. We study various pruning strategies with our objective being reduction in computational burden and conclude that transform decomposition is the most appropriate pruning strategy. Using this pruning technique, worst-case computational savings of 10% and 5% can be achieved for applications that use filter lengths on the order of a few hundreds and a few thousands, respectively. In delay-sensitive applications, substantially more savings can be effected by pruning.
The Limitations of Estimation
Authors: Linda M. Laird
Venue: IT Professional (2006)

Estimation is a crucial element of software project planning. Unfortunately, there are inherent limitations in the ability to estimate projects accurately due to the inherent uncertainties in software projects.
One story, one flow: Hidden Markov Story Models for multilingual multidocument summarization
Authors: Pascale Fung, Grace Ngai
Venue: ACM Transactions on Speech and Language Processing (TSLP) (2006)

This article presents a multidocument, multilingual, theme-based summarization system based on modeling text cohesion (story flow). Conventional extractive summarization systems which pick out salient sentences to include in a summary often disregard any flow or sequence that might exist between these sentences. We argue that such inherent text cohesion exists and is (1) specific to a particular story and (2) specific to a particular language. Documents within the same story, and in the same language, share a common story flow, and this flow differs across stories, and across languages. We propose using Hidden Markov Models (HMMs) as story models. An unsupervised segmental K-means method is used to iteratively cluster multiple documents into different topics (stories) and learn the parameters of parallel Hidden Markov Story Models (HMSM), one for each story. We compare story models within and across stories and within and across languages (English and Chinese). The experimental results support our &ldquo;one story, one flow&rdquo; and &ldquo;one language, one flow&rdquo; hypotheses. We also propose a Na&iuml;ve Bayes classifier for document summarization. The performance of our summarizer is superior to conventional methods that do not incorporate text cohesion information. Our HMSM method also provides a simple way to compile a single metasummary for multiple documents from individual summaries via state labeled sentences.
Virtual object manipulation using a mobile phone
Authors: Anders Henrysson, Mark Billinghurst, Mark Ollila
Venue: Proceedings of the 2005 international conference on Augmented tele-existence (2005)

Augmented Reality (AR) on mobile phones has reached a level of maturity where it can be used as a tool for 3D object manipulation. In this paper we look at user interface issues where an AR enabled mobile phone acts as an interaction device. We discuss how traditional 3D manipulation techniques apply to this new platform. The high tangibility of the device and its button interface makes it interesting to compare manipulation techniques. We describe AR manipulation techniques we have implemented on a mobile phone and present a small pilot study evaluating these methods.
Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database
Authors: Susan Maskery, Yonghong Zhang, Hai Hu, Craig Shriver, Jeffrey Hooke, Michael Liebman
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

Over the past five years the Clinical Breast Care Project (CBCP) has amassed a significant patient database and tissue repository related to breast disease and breast cancer. We have begun mining this unique data source (i.e. life history questionnaire data, pathology reports, analysis of blood and tissue samples) to examine interactions between known risk factors for breast cancer development (i.e. menopausal status, parity, etc.) with breast disease and cancer incidence in our patient population. From these initial forays into analyzing the CBCP's data repository, we have begun to develop protocols for data mining. In particular, a crucial first step is to quantify interactions between variables of interest prior to any specific significance tests relating individual variables to risk of a clinical result. For this purpose, we find Bayesian network analysis the most useful method for exploration of data interactions. To illustrate this point, this abstract details an investigation into the effect of caffeine consumption on breast cancer incidence in our CBCP population. Based on our experience with this and other studies we strongly recommend Bayesian network analysis of all variables of interest as an initial data exploration tool.
Adaptive Perceptual Degradation Based on Video Usage
Authors: Alexander Wong, William Bishop
Venue: Proceedings of the Eighth IEEE International Symposium on Multimedia (2006)

The emergence of online digital media sales services has given rise to the issue of online digital rights management (DRM). This paper presents a novel approach to online digital rights management and video content protection through the use of adaptive perceptual degradation based on usage. The proposed system facilitates the sharing of purchased digital videos between users while offering an incentive for users to acquire original video content through legal means. This is accomplished by adaptively degrading the quality of the digital video content based on a number of usage factors such as the number of times viewed and the number of copies made. Experimental results demonstrate the effectiveness of this system in striking a balance between the freedom of file sharing and content protection.
Design of the Intelligent Control System of Telerobot Based on Virtual Reality
Authors: Feng Cui, Minglu Zhang, Shenghui Guo
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

An intelligent control system of layered structure for telerobots is established and described in detail. It is a semi-autonomous teleoperation platform which allows control to be shared between the intelligent decision system in the virtual environment and operators throughout a mission. The intelligent decision system will plan and control locomotion of the mobile robot according to the mission request. Because modeling error of the virtual environment and slide phenomena of wheels exist inevitably, the mobile robot can not reach the desired destination precisely by autonomous mode. Operators must directly adjust pose of the robot with remote video abidance to eliminate these errors. Telecommands and videos are transmitted via WLAN (Wireless LAN) with high gain antenna which brings broad bandwidth and far communication distance. Experiments prove that the intelligent decision platform is reliable and efficient. With intervention of operators away from five kilometres, the mobile robot can reach the desired destination successfully and accomplish the planned task smoothly.
Symbolic negotiation revisited
Authors: Peep Küngas, Mihhail Matskin
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

In this paper we propose a formalism for symbolic negotiation. We regard symbolic negotiation as cooperative problem solving (CPS), which is based on symbolic reasoning and is extended with negotiation-specific rules. The underlying CPS formalism was previously presented in [3]. Here we extend the results and position symbolic negotiation according to other distributed problem solving mechanisms.
Develop a telecommunication service system using service-oriented architecture
Authors: Sun Wenhui, Liu Feng, Zhang Jinyu, Dai Gang
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

In this paper, we present the function of a telecommunication service system and its traditional architecture. Combining a telecommunication service system as developmental requirement of TOM, we present a prototype system using a service-oriented architecture. We also give a business model of this telecommunication service system.
Component composition through architectural patterns for problem frames
Authors: Christine Choppy, Denis Hatebur, Maritta Heisel
Venue: Proceedings of the XIII Asia Pacific Software Engineering Conference (2006)

In this paper, we present a pattern-based software development process using problem frames and corresponding architectural patterns. In decomposing a complex problem into simple subproblems, the relationships between the subproblems are recorded explicitly. Based on this information, we give guidelines on how to derive the software architecture for the overall problem from the software architectures of the simple subproblems.
Entropy Evaluation Model of Enterprises Performance Based on Supply Chain Management Theory
Authors: Jun Xu, Qi Zeng
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

On the basis of analyzing the shortcomings of the traditional performance evaluation, the evaluation-index system of enterprises performance based on supply chain management was built. In order to be able to evaluate objectively and scientifically enterprises performance, entropy appraisal model was set up, which was an objective empower approach. Then it was utilized to evaluate the performance of five listed electronic enterprises according to the evaluation results. Finally, stronger guidance was given in conclusions.
Test Optimization Using Software Virtualization
Authors: Swaminathan Seetharaman, Krishna Murthy B.V.S.
Venue: IEEE Software (2006)

The availability of testing infrastructure is a major factor in product development projectcosts. Software virtualization is a powerful mechanism for simulating a test setup on a few desktopsthat would otherwise require "real" equipment. It has shown a savings of about 30 percent of overallproduct development costs by executing 80 percent of unit testing and 25 percent of grey- andblack-box testing in the desktop environment. Both open source and commercial products are availableto support it.
Intelligent Optimization via Learnable Evolution Model
Authors: Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

A new method for optimizing complex functions and systems is described that employs Learnable Evolution Model (LEM), a form of non-Darwinian evolutionary computation guided by machine learning. LEM's main novelties are operators for creating new individuals that include hypothesis generation, which learns rules indicating subareas in the search space likely containing the optimum, and hypothesis instantiation, which populates these subareas with new candidate solutions. LEM3, the newest and most advanced implementation of learnable evolution, is briefly described and experimentally compared with other evolutionary computation programs on selected function optimization problems. We also describe two specialized LEM-based systems for heat exchanger optimization.
Meta-Evaluation of Image Segmentation Using Machine Learning
Authors: Hui Zhang, Sharath Cholleti, Sally A. Goldman, Jason E. Fritts
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.
A concentration bound for the longest increasing subsequence of a randomly chosen involution
Authors: Marcos Kiwi
Venue: Discrete Applied Mathematics (2006)

In this short note we prove a concentration result for the length of the longest increasing subsequence (LIS) of a randomly and uniformly chosen involution of {1 ..... s}.
Comparison of Several Classifiers for the Detection of Polluting Smokes
Authors: D. GACQUER, F. DELMOTTE, V. DELCROIX, S. PIECHOWIAK
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

This paper addresses the pollution detection problem by using a camera and analyzing the pictures. A camera is used to record visual scenes around complex plants. Then several signals are computed to describe the pictures. Our aim is to detect among the various clouds if there are polluting smokes. We assume in this paper that the signals are useful to classify the clouds and that we do not need other data. In this paper two types of classifiers are studied: bayesian networks and a k-nearest neighbour classifier.
Two Phase Reverse Neural Network Based Human Vital Sign Prediction System
Authors: Insung Jung, Seung Chan Park, Gi-Nam Wang
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

The objective of this paper is to propose a two phase reverse neural network (NN) model to predict sequential human bio signal data for decreasing the risk of acute and likely fatal diseases and syndromes in patients (e.g., COPD and hypertension) based on a simulated learning algorithm. Our approach uses a two phase sequential neural network with back-propagation learning. The modeling is done using time-varying inputs such as heart rate, SpO_2, and blood pressure as measured and communicated by wireless devices. The output of the prediction system results into distinct classification levels for patient's condition through sequential points. Such a prediction facilitates an early detection of exacerbating risk factors thereby saving a sudden death and other high risk diseases. The system is plausible to be used for the prevention and management of high risk diseases.
Cinematized Reality: Cinematographic 3D Video System for Daily Life Using Multiple Outer/Inner Cameras
Authors: Hansung Kim, Ryuuki Sakamoto, Kiyoshi Kogure, Itaru Kitahara
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

The purpose of Cinematized Reality is to record unexpected moments in people's lives and create movies that look as if they were created from real, expertly captured film footage. The approach toward Cinematized Reality is to generate free-view video streams from multiple videos. The proposed system reconstructs 3D models of the capturing space using outer environmental cameras and an inner omni-directional camera, and then generates realistic video of those objects from a designated viewpoint by controlling a virtual camera as an expert would. This prototype system follows the grammar of film language and applies expert knowledge to generate desirable film footage using a sequence of shots taken by the virtual camera.
Artificial kinematics and simple stabilization of solid-shell elements occurring in highly constrained situations and applications in composite sheet forming simulation
Authors: M. Harnau, K. Schweizerhof
Venue: Finite Elements in Analysis and Design (2006)

Well-known problems which occur for mixed or underintegrated element formulations are artificial kinematics resp. numerical instabilities. These problems are also observed for 3D-shell elements as the so-called solid-shell elements especially when the loading leads to a homogeneous stress state. To prevent the elements from artificial kinematical failure a simple procedure is applied which allows to identify critical states. Then the non-necessary element modifications responsible for the numerical instabilities can be taken out.A special application for the solid-shell elements which includes strong constraining are sheet forming problems with high stretching and large local contact pressure especially for layered composite sheets which cannot be modeled with standard 2D-shells. In the second part some numerical examples for the simulation of the forming process for pasted composite sheets are shown.