ppxscal/embedding_network huggingface dataset:
Parametrization of stable systems from impulse response data
Authors: S. Unnikrishna Pillai
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

The paper addresses the problem of identifying the class of all stable system transfer functions that interpolate the given partial impulse response sequence. In this context, classical Pade approximations that are also stable, are shown to be a special case of this general formulation. The theory developed in this connection is utilized to obtain a new criterion for determining the model order and system parameters for rational systems, and, further, to generate nonminimum phase optimal stable rational approximations of nonrational systems from their impulse response sequences. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A Layered Behavioural Model of Platelets
Authors: Steve Schneider, Helen Treharne, Ana Cavalcanti, Jim Woodcock
Venue: Proceedings of the 11th IEEE International Conference on Engineering of Complex Computer Systems (2006)

There is great interest in the application of nanotechnology to medicine, but concerns for safety are paramount. We present a modelling technique based on CSP and B as a starting point for simulation of networks of nanorobots. The model and the simulations are central features of our proposed approach to the construction of safety cases for nanomedicine applications, and complex networks of cooperating components in general. Our work is based on a case study: the clotting behaviour of (artificial) platelets. We present a model, and discuss its analysis and uses.
Semantic-Shift for Unsupervised Object Detection
Authors: David Liu, Tsuhan Chen
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

The bag of visual words representation has attracted a lot of attention in the computer vision community. In particular, Probabilistic Latent Semantic Analysis (PLSA) has been applied to object recognition as an unsupervised technique built on top of the bag of visual words representation. PLSA, however, does not explicitly consider the spatial information of the visual words. In this paper, we propose an iterative technique, where a modified form of PLSA provides location and scale estimates of the foreground object through the estimated latent semantic. In return, the updated location and scale estimates will improve the estimate of the latent semantic. We call this iterative algorithm Semantic-Shift. We show results with significant improvements over PLSA.
Simulation output analysis: a tutorial based on one research thread
Authors: Bruce Schmeiser
Venue: Proceedings of the 36th conference on Winter simulation (2004)

In reviewing topics in simulation output analysis, we advocate univariate analysis, micro/macro replications based on fixed sample sizes, overlapping batches, batch sizes based on mean squared error, dynamic batch sizes, and a concise format for reporting results.
An Internet Based Intelligent Argumentation System for Collaborative Engineering Design
Authors: Xiaoqing Frank Liu, Samir Raorane, Man Zheng, Ming Leu
Venue: Proceedings of the International Symposium on Collaborative Technologies and Systems (2006)

Modern product design is a very complicated process which involves groups of designers, manufacturers, suppliers, and customer representatives. Conflicts are unavoidable in collaboration among multiple stakeholders, who have different objectives, requirements, and priorities. Unfortunately, current web-based collaborative engineering design systems do not support collaborative conflict resolution. In this paper, we will develop an intelligent computational argumentation model to enable management of a large scale argumentation network, and resolution of conflicts based on argumentation from many participants. A web-based intelligent argumentation tool is developed as a part of a web-based collaborative engineering design system based on the above model to resolve conflicts over the internet by enabling selection of the most favored design alternative in the design argumentation from multiple perspectives in collaborative engineering design.
Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection
Authors: Ming-Yuan Cho, Tsair-Fwu Lee, Shih-Wei Kau, Chin-Shiuh Shieh, Chao-Ji Chou
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

For the purpose of fault diagnosis of power transformers, a novel approach based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) is presented in the paper. The proposed approach is distinguished by features and kernel parameters selection using clonal selection algorithms (CSA). It is capable of filtering out irrelevant input features, leading to improve prediction accuracy. As revealed in the experimental results, the proposed approach outperforms previous ones in both classification accuracy and computational efficiency.
Adaptive antenna array pattern synthesis using recursive minimum-norm solution with constraint selection and phase optimization
Authors: Kai-Bor Yu
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

An algorithm has been developed to provide the uplink received antenna with beamforming capability for selective earth coverage and jamming protection for military satellite communications applications. The problem can be formulated as to determine the control weight to meet the user gain and jammer null requirements in their known directions subject to the norm constraint. A recursive procedure has been developed which provides flexibility in the determination of the constraint vector, the constraint order and the constraint phase optimization. The recursion can also be implemented as updating of the null space. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Buddy bots: how turing's fast friends are undermining consumer privacy
Authors: Ian R. Kerr, Marcus Bornfreund
Venue: Presence: Teleoperators and Virtual Environments (2005)

Intelligent agents are currently being deployed in virtual environments to enable interaction with consumers in furtherance of various corporate strategies involving marketing, sales, and customer service. Some online businesses have recently begun to adopt automation technologies that are capable of altering both their own, and consumers', legal rights and obligations. In a rapidly evolving field known as affective computing, the creators of some automation technologies are utilizing various principles of cognitive science and artificial intelligence to generate avatars capable of garnering consumer trust. Unfortunately, this trust has been exploited by some to undertake extensive, clandestine consumer profiling under the guise of friendly conversation. Buddy bots and other such applications have been used by businesses to collect valuable personal information and private communications without lawful consent. This article critically examines such practices and provides basic consumer protection principles, an adherence to which promises to generate a more socially responsible vision of the application of artificial intelligence in automated electronic commerce.
Neural Correlates of Auditory Repetition Priming: Reduced fMRI Activation in the Auditory Cortex
Authors: Dafna Bergerbest
Venue: Journal of Cognitive Neuroscience (2004)

Repetition priming refers to enhanced or biased performance with repeatedly presented stimuli. Modality-specific perceptual repetition priming has been demonstrated behaviorally for both visually and auditorily presented stimuli. In functional neuroimaging studies, repetition of visual stimuli has resulted in reduced activation in the visual cortex, as well as in multimodal frontal and temporal regions. The reductions in sensory cortices are thought to reflect plasticity in modality-specific neocortex. Unexpectedly, repetition of auditory stimuli has resulted in reduced activation in multimodal and visual regions, but not in the auditory temporal lobe cortex. This finding puts the coupling of perceptual priming and modality-specific cortical plasticity into question. Here, functional magnetic resonance imaging was used with environmental sounds to reexamine whether auditory priming is associated with reduced activation in the auditory cortex. Participants heard environmental sounds (e.g., animals, machines, musical instruments, etc.) in blocks, alternating between initial and repeated presentations, and decided whether or not each sound was produced by an animal. Repeated versus initial presentations of sounds resulted in repetition priming (faster responses) and reduced activation in the right superior temporal gyrus, bilateral superior temporal sulci, and right inferior prefrontal cortex. The magnitude of behavioral priming correlated positively with reduced activation in these regions. This indicates that priming for environmental sounds is associated with modification of neural activation in modality-specific auditory cortex, as well as in multimodal areas.
Solution methods for the p-median problem: An annotated bibliography
Authors: J. Reese
Venue: Networks (2006)

The p-median problem is a network problem that was originally designed for, and has been extensively applied to, facility location. In this bibliography, we summarize the literature on solution methods for the uncapacitated and capacitated p-median problem on a network. &copy; 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(3), 125&ndash;142 2006
Multiscale finite-volume method for compressible multiphase flow in porous media
Authors: Ivan Lunati, Patrick Jenny
Venue: Journal of Computational Physics (2006)

The Multiscale Finite-Volume (MSFV) method has been recently developed and tested for multiphase-flow problems with simplified physics (i.e. incompressible flow without gravity and capillary effects) and proved robust, accurate and efficient. However, applications to practical problems necessitate extensions that enable the method to deal with more complex processes. In this paper we present a modified version of the MSFV algorithm that provides a suitable and natural framework to include additional physics. The algorithm consists of four main steps: computation of the local basis functions, which are used to extract the coarse-scale effective transmissibilities; solution of the coarse-scale pressure equation; reconstruction of conservative fine-scale fluxes; and solution of the transport equations. Within this framework, we develop a MSFV method for compressible multiphase flow. The basic idea is to compute the basis functions as in the case of incompressible flow such that they remain independent of the pressure. The effects of compressibility are taken into account in the solution of the coarse-scale pressure equation and, if necessary, in the reconstruction of the fine-scale fluxes. We consider three models with an increasing level of complexity in the flux reconstruction and test them for highly compressible flows (tracer transport in gas flow, imbibition and drainage of partially saturated reservoirs, depletion of gas-water reservoirs, and flooding of oil-gas reservoirs). We demonstrate that the MSFV method provides accurate solutions for compressible multiphase flow problems. Whereas slightly compressible flows can be treated with a very simple model, a more sophisticate flux reconstruction is needed to obtain accurate fine-scale saturation fields in highly compressible flows.
A Joint Distribution Approach for Audio Signal Discrimination
Authors: Abdullah I. Al-Shoshan
Venue: Proceedings of the conference on Geometric Modeling and Imaging: New Trends (2006)

The purpose of audio signal discrimination is needed to build two different libraries: speech library and music library, from a stream of sounds. The classification algorithms of audio signal can be divided into three categories: time-domain, frequency-domain, and time-frequency domain approaches. The first two approaches were discussed thoroughly in literature. Since audio signals are considered nonstationary, in general, these two approaches may fail in reflecting the behavior of each signal correctly. Therefore, in this paper, a joint distribution approach, the evolutionary spectrum (ES), is introduced and proposed for the discrimination purpose. To show the effectiveness of the proposed method, some comparison examples with classical methods is done.
rDCF: A Relay-Enabled Medium Access Control Protocol for Wireless Ad Hoc Networks
Authors: Hao Zhu, Guohong Cao
Venue: IEEE Transactions on Mobile Computing (2006)

It is well known that IEEE 802.11 provides a physical layer multirate capability and, hence, MAC layer mechanisms are needed to exploit this capability. Several solutions have been proposed to achieve this goal. However, these solutions only consider how to exploit good channel quality for the direct link between the sender and the receiver. Since IEEE 802.11 supports multiple transmission rates in response to different channel conditions, data packets may be delivered faster through a relay node than through the direct link if the direct link has low quality and low rate. In this paper, we propose a novel MAC layer relay-enabled distributed coordination function (DCF) protocol, called rDCF, to further exploit the physical layer multirate capability. We design a protocol to assist the sender, the relay node, and the receiver to reach an agreement on which data rate to use and whether to transmit the data through a relay node. Considering various issues, such as, bandwidth utilization, channel errors, and security, we propose techniques to further improve the performance of rDCF. Simulation results show that rDCF can significantly reduce the packet delay, improve the system throughput, and alleviate the impact of channel errors on fairness.
Multi-Robot Positioning Model: Multi-Agent Approach
Authors: Satoshi Kataoka, Shinichi Honiden
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)

Dynamic coverage is a problem of multi-robot systems based on wireless ad-hoc networks. The issue of dynamic coverage occurs notably in post-disaster survivor rescue, search operation, and planet exploration. In this paper, we introduce a novel algorithm of dynamic coverage in a realistically restricted environment for robots. This approach improves efficiency of moving around and amount of communication in a simulation environment. The paper presents comprehensive experimental results and discusses future research directions related to dynamic coverage.
Subspace-based method for joint range and DOA estimation of multiple near-field sources
Authors: Yuntao Wu, Lin Ma, Chaohuan Hou, Guangbin Zhang, Jun Li
Venue: Signal Processing (2006)

A subspace-based method for joint range and DOA (direction-of-arrival) estimation of multiple near-field sources is presented. The proposed method uses fourth-order cumulants, and the range and DOA parameters are directly given by the eigenvectors and eigenvalues of a constructed matrix. Compared with some available methods, the 2D parameters are automatically paired and the loss of array aperture is also reduced. Performance evaluation via computer simulations is included to demonstrate the effectiveness of the proposed algorithm.
A computing model of pressure distribution from tight underwear
Authors: Xiaonan Luo, Huimin Luo
Venue: Journal of Computational and Applied Mathematics (2006)

Based on the theory of membrane of huge deformation in elasticity, a computable model is presented to solve the problem of pressure distribution on a human body from tight underwear. This model is steady and reliable. With the help of this model, it is easy to solve the problem of describing wearing-dressed effect in Integrated Garments CAD system.
Shifting epistemologies: examining student understanding of new models of knowledge and learning
Authors: Katerine Bielaczyc, Peter Blake
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

New models of teaching and learning designed to address the demands of 21st century education require shifts in classroom norms and practices. This paper examines a group of students and teachers who implemented the Knowledge Forum model (Scardamalia, 2004) over the course of two years. Interviews with the students are used to assess three epistemological changes identified as critical in the Knowledge Forum model: a shift from teacher-directed activities to student epistemic agency, a shift from right and wrong answers to the notion of improvable ideas, and a shift from individual to community knowledge and collective responsibility. There are indications that shifts are indeed taking place in students' views toward their own agency in the learning process, the dynamic nature of knowledge, and the importance of community. The work also identifies challenges faced by teachers in implementing new models of learning that can inform teacher pedagogy.
Learnable behavioural model for autonomous virtual agents: low-level learning
Authors: Toni Conde, Daniel Thalmann
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

In this paper, we propose a new integration approach for simulation and behaviour in the learning context that is able to coherently manage the shared virtual environment for the simulation of autonomous virtual agents. Our low-level learning technique has proved fast, simple and robust. It is also able to automatically learn behavioural models for difficult tasks. Thus, we believe it will be more useful to the computer graphics community than a technique based on the classical Q-learning approach. The results are illustrated in two case studies that require effective coordination of behaviours.
Validation of a time-of-supply inventory policy through simulation
Authors: Benjamin Martin, James Francis
Venue: Proceedings of the 37th conference on Winter simulation (2005)

A popular safety-inventory policy utilized in the apparel industry is time-of-supply. Time-of-supply specifies inventory held to protect against forecast error and supply disruptions in terms of safety-time rather than safety-stock. Determining the proper safety-time is critical to ensure customer service. This case study presents a method for determining safety-time for raw materials in an apparel manufacturing operation. Additionally, the results of a simulation study that validated the time-of-supply inventory policy are presented.
Tracking MUSes and Strict Inconsistent Covers
Authors: Eric Gregoire, Bertrand Mazure, Cedric Piette
Venue: Proceedings of the Formal Methods in Computer Aided Design (2006)

In this paper, a new heuristic-based approach is introduced to extract minimally unsatisfiable subformulas (in short, MUSes) of SAT instances. It is shown that it often outperforms current competing methods. Then, the focus is on inconsistent covers, which represent sets of MUSes that cover enough independent sources of infeasibility for the instance to regain satisfiability if they were repaired. As the number of MUSes can be exponential with respect to the size of the instance, it is shown that such a concept is often a viable trade-off since it does not require us to compute all MUSes but provides us with enough mutually independent infeasibility causes that need to be addressed in order to restore satisfiability.
BYY Harmony Learning on Finite Mixture: Adaptive Gradient Implementation and A Floating RPCL Mechanism
Authors: Jinwen Ma, Le Wang
Venue: Neural Processing Letters (2006)

In tackling the learning problem on a set of finite samples, Bayesian Ying-Yang (BYY) harmony learning has developed a new learning mechanism that makes model selection implemented either automatically during parameter learning or in help of evaluating a new class of model selection criteria. In this paper, parameter learning with automated model selection has been studied for finite mixture model via an adaptive gradient learning algorithm for BYY harmony learning on a specific bidirectional architecture (BI-architecture). Via theoretical analysis, it has shown that the adaptive gradient learning implements a mechanism of floating rival penalized competitive learning (RPCL) among the components in the mixture. Also, the simulation results are demonstrated well for the adaptive gradient algorithm on the sample data sets from Gaussian mixtures with certain degree of overlap. Moreover, the adaptive gradient algorithm is applied to classification of the Iris data and unsupervised color image segmentation.
Simulation and the semantic web
Authors: John A. Miller, Gregory Baramidze
Venue: Proceedings of the 37th conference on Winter simulation (2005)

One of the missions of the Semantic Web is to put more knowledge on the Web in an organized fashion and link it to other information and data sources. Three successively more capable languages are (or will soon be) provided for this: RDF, OWL, and SWRL. This paper makes a case for using all three for the domain of modeling and simulation. Based on experience developing the Discrete-event Modeling Ontology (DeMO) some observations on the issues and challenges involved in creating such ontologies are presented. An approach for decomposing models into behavioral and observable parts, a la Hidden Markov Models, which can make ontologies smaller and easier to understand, is also discussed.
Integrating Co-Occurrence and Spatial Contexts on PatchBased Scene Segmentation
Authors: Florent Monay, Pedro Quelhas, Jean-Marc Odobez, Daniel Gatica-Perez
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

We present a novel approach for contextual segmentation of complex visual scenes, based on the use of bags of local invariant features (visterms) and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific learned aspects correlate with the semantic classes, which resolves some cases of visual polysemy, and (2) by formalizing the notion that scene context is image-specific -what an individual visterm represents depends on what the rest of the visterms in the same bag represent too-. We demonstrate the validity of our approach on a man-made vs. natural visterm classification problem. Experiments on an image collection of complex scenes show that the approach improves region discrimination, producing satisfactory results, and outperforming a non-contextual method. Furthermore, through the later use of a Markov Random Field model, we also show that co-occurrence and spatial contextual information can be conveniently integrated for improved visterm classification.