ppxscal/embedding_network huggingface dataset:
Simulation-based assessment of order release strategies for a distributed shifting bottleneck heuristic
Authors: Lars Mönch
Venue: Proceedings of the 37th conference on Winter simulation (2005)

In this paper, we investigate the influence of several order release strategies on the performance of a distributed shifting bottleneck heuristic. The shifting bottleneck heuristic is a decomposition approach that solves the overall scheduling problem by solving a sequence of tool group scheduling problems and determines the overall solution by using a disjunctive graph. We discuss a distributed version of the original shifting bottleneck heuristic. By using a hierarchical approach we first assign planned ready and completion dates to all lots with respect to a certain work area where a work area is defined as a set of tool groups. We study several order release strategies. It turns out that the distributed shifting bottleneck heuristic performs well compared to dispatching rules only in high loaded job shops. We present the results of computational experiments.
Integrating Value-Directed Compression and Belief Space Analysis for POMDP Decomposition
Authors: Xin Li, William K. Cheung, Jiming Liu
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Partially observable Markov decision process (POMDP) is a commonly adopted framework to model planning problems for agents to act in a stochastic environment. Obtaining the optimal policy of POMDP for large-scale problems is known to be intractable, where the high dimension of its belief state is one of the major causes. The use of the compression approach has recently been shown to be promising in tackling the curse of dimensionality problem. In this paper, a novel value-directed belief compression technique is proposed,together with clustering of belief states for further reducing the underlying computational complexity. We first cluster some sampled belief states into disjoint partitions and then apply a non-negative matrix factorization (NMF) based projection to each belief state cluster for dimension reduction. We then compute the optimal policy is then computed using a pointed-based value iteration algorithm defined in the low-dimensional projected belief state space. The proposed algorithm has been evaluated using a synthesized navigation problem. Solutions with quality comparable to the original POMDP were obtained at a much lower computational cost.
Numerical treatment of an initial-boundary value problem for fractional partial differential equations
Authors: Mariusz Ciesielski, Jacek Leszczynski
Venue: Signal Processing (2006)

This paper deals with numerical solutions to a partial differential equation of fractional order. Generally this type of equation describes a transition from anomalous diffusion to transport processes. From a phenomenological point of view, the equation includes at least two fractional derivatives: spatial and temporal. In this paper we proposed a new numerical scheme for the spatial derivative, the so-called Riesz-Feller operator. Moreover, using the finite difference method, we show how to employ this scheme in the numerical solution of fractional partial differential equations. In other words, we considered an initial-boundary value problem in one-dimensional space. In the final part of this paper some numerical results and plots of simulations are shown as examples.
A connectionist computational model for epistemic and temporal reasoning
Authors: Artur S. d'Avila Garcez, Luís C. Lamb
Venue: Neural Computation (2006)

The importance of the efforts to bridge the gap between the connectionist and symbolic paradigms of artificial intelligence has been widely recognized. The merging of theory (background knowledge) and data learning (learning from examples) into neural-symbolic systems has indicated that such a learning system is more effective than purely symbolic or purely connectionist systems. Until recently, however, neural-symbolic systems were not able to fully represent, reason, and learn expressive languages other than classical propositional and fragments of first-order logic. In this article, we show that nonclassical logics, in particular propositional temporal logic and combinations of temporal and epistemic (modal) reasoning, can be effectively computed by artificial neural networks. We present the language of a connectionist temporal logic of knowledge (CTLK). We then present a temporal algorithm that translates CTLK theories into ensembles of neural networks and prove that the translation is correct. Finally, we apply CTLK to the muddy children puzzle, which has been widely used as a testbed for distributed knowledge representation. We provide a complete solution to the puzzle with the use of simple neural networks, capable of reasoning about knowledge evolution in time and of knowledge acquisition through learning.
Efficient blind estimation and equalization of non-minimum phase communication channels via the use of a zero forcing equalizer
Authors: Yun Ye, Saman S. Abeysekera
Venue: Signal Processing (2006)

Via the use of a zero forcing equalizer, the blind estimation of a finite impulse response (FIR), non-minimum phase (NMP) channel is discussed in this paper. Two efficient and reliable blind estimation algorithms are proposed here. One is based on the combination of second-order statistics (SOS) and the kurtosis property of the transmitted signal. The other utilizes the SOS information and the finite alphabet (FA) knowledge of the transmitted signal. SOS based methods provide efficient estimation of channel zeros from a very small number of samples, but the estimates are phase blind. The kurtosis property and the FA information can be used to resolve the ambiguity in system zero location. It is also shown that the equalizer output could be exploited recursively to improve the estimation accuracy using the FA property. As all the available information are used, the proposed methods achieve a very high accuracy in blind channel estimation. Performance of the estimation methods is also discussed. As the method inherently uses an equalizer, separate equalization is not necessary.
An Enhanced Gnutella for Ad-Hoc Networks
Authors: Hyun-Duk Choi, Ho-Hyun Park, Miae Woo
Venue: Proceedings of the International Conference on Systems and Networks Communication (2006)

In recent years, Internet has witnessed the introduction of many peer-to-peer systems designed to implement large scale resource sharing. These systems exhibit interesting features like self configuration, self-healing and complete decentralization, which make the systems appealing for deployment in ad hoc environments as well. This paper enhances Gnutella with peer-node hierarchy to achieve better performance with less overhead in the ad hoc network. The proposed system introduces ultrapeer selection scheme based on the metric values and a proactive communication mechanism among ultrapeers. According to the simulation results, the proposed system gives better performance than Gnutella in terms of query success rate, query response time, and overhead.
Efficient RT-level fault diagnosis
Authors: Ozgur Sinanoglu, Alex Orailoglu
Venue: Journal of Computer Science and Technology (2005)

Increasing IC densities necessitate diagnosis methodologies with enhanced defect locating capabilities. Yet the computational effort expended in extracting diagnostic information and the stringent storage requirements constitute major concerns due to the tremendous number of faults in typical ICs. In this paper, we propose an RT-level diagnosis methodology capable of responding to these challenges. In the proposed scheme, diagnostic information is computed on a grouped fault effect basis, enhancing both the storage and the computational aspects. The fault effect grouping criteria are identified based on a module structure analysis, improving the propagation ability of the diagnostic information through RT modules. Experimental results show that the proposed methodology provides superior speed-ups and significant diagnostic information compression at no sacrifice in diagnostic resolution, compared to the existing gate-level diagnosis approaches.
VOGA: Variable Ordering Genetic Algorithm for Learning Bayesian Classifiers
Authors: Edimilson Batista dos Santos, Estevam Rafael Hruschka Junior
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

This work proposes a hybrid approach to help the process of learning a Bayesian Classifier (BC) from data. The proposed method named VOGA (and its variant VOGA+) uses a Genetic Algorithm to optimize the BC learning process by means of the identification of an adequate variables ordering. The main contribution of VOGA and VOGA+ is the use information about the class variable when defining the most suitable variable ordering. Trying to optimize the GA initial population, VOGA+ ranks the attributes based on the class variable. Experiments performed in a number of datasets revealed that both methods are promising and VOGA+ tends to be favored domains having higher number of variables.
Large alphabets and incompressibility
Authors: Travis Gagie
Venue: Information Processing Letters (2006)

We briefly survey some concepts related to empirical entropy--normal numbers, de Bruijn sequences and Markov processes-- and investigate how well it approximates Kolmogorov complexity. Our results suggest lth-order empirical entropy stops being a reasonable complexity metric for almost all strings of length m over alphabets of size n about when nl surpasses m.
A loopless Gray code for rooted trees
Authors: James Korsh, Paul Lafollette
Venue: ACM Transactions on Algorithms (TALG) (2006)

Beyer and Hedetniemi [1980] gave the first constant average-time algorithm for the generation of all rooted trees with n nodes. This article presents the first combinatorial Gray code for these trees and a loopless algorithm for its generation.
Use of Instance Typicality for Efficient Detection of Outliers with Neural Network Classifiers
Authors: Shirish S. Sane, Ashok A. Ghatol
Venue: Proceedings of the 9th International Conference on Information Technology (2006)

Detection of outliers is one of the data pre-processing tasks. In all the applications, outliers need to be detected to enhance the accuracy of the classifiers. Several different techniques, such as statistical, distance-based and deviation-based outlier detection exist to detect outliers. Many of these techniques use filter method. A wrapper method using the concept of instance typicality may also be used to detect outliers. This paper deals with a new wrapper method that builds an initial model using neural networks and treats values at the output of neurons in the output layer as the typicality scores. Instances with lowest output values are treated as potential outliers. In addition, the method is also useful to build compact and accurate classifiers by selecting a few most typical instances resulting in significant reduction in storage space. The method is generic and thus can also be used for instance selection with any kind of classifiers. Resultant compact models are useful for imputation of missing values.
Application Layer Definition and Analyses of Controller Area Network Bus for Wire Harness Assembly Machine
Authors: Hui Guo, Ying Jiang
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)

With the feature of multi-master bus access, nondestructive contention-based arbitration and flexible configuration, Controller Area Network (CAN) bus is applied into the control system of Wire Harness Assembly Machine (WHAM). To accomplish desired goal, the specific features of the CAN bus is analyzed by compared with other field buses and the functional performances in the CAN bus system of WHAM is discussed. Then the application layer planning of CAN bus for dynamic priority is presented. The critical issue for the use of CAN bus system in WHAM is the data transfer rate between different nodes. So processing efficient model is introduced to assist analyzing data transfer procedure. Through the model, it is convenient to verify the real time feature of the CAN bus system in WHAM.
Visual Speech Recognition Method Using Translation, Scale and Rotation Invariant Features
Authors: Wai Chee Yau, Dinesh Kant Kumar, Sridhar Poosapadi Arjunan
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

This paper reports on a visual speech recognition method that is invariant to translation, rotation and scale. Dynamic features representing the mouth motion is extracted from the video data by using a motion segmentation technique termed as motion history image (MHI). MHI is generated by applying accumulative image differencing technique on the sequence of mouth images. Invariant features are derived from the MHI using feature extraction algorithm that combines Discrete Stationary Wavelet Transform (SWT) and moments. A 2-D SWT at level one is applied to decompose MHI to produce one approximate and three detail sub images. The feature descriptors consist of three moments (geometric moments, Hu moments and Zernike moments) computed from the SWT approximate image. The moments features are normalized to achieve the invariance properties. Artificial neural network (ANN) with back propagation learning algorithm is used to classify the moments features. Initial experiments were conducted to test the sensitivity of the proposed approach to rotation, translation and scale of the mouth images and obtained promising results.
Contour-Based Structure from Reflection
Authors: Po-Hao Huang, Shang-Hon Lai
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

In this paper, we propose a novel contour-based algorithm for 3D object reconstruction from a single uncalibrated image acquired under the setting of two plane mirrors. With the epipolar geometry recovered from the image and the properties of mirror reflection, metric reconstruction of an arbitrary rigid object is accomplished without knowing the camera parameters and the mirror poses. For this mirror setup, the epipoles can be estimated from the correspondences between the object and its reflection, which can be established automatically from the tangent lines of their contours. By using the property of mirror reflection as well as the relationship between the mirror plane normal with the epipole and camera intrinsic, we can estimate the camera intrinsic, plane normals and the orientation of virtual cameras. The positions of the virtual cameras are determined by minimizing the distance between the object contours and the projected visual cone for a reference view. After the camera parameters are determined, the 3D object model is constructed via the image-based visual hulls (IBVH) technique. The 3D model can be refined by integrating the multiple models reconstructed from different views. The main advantage of the proposed contour-based Structure from Reflection (SfR) algorithm is that it can achieve metric reconstruction from an uncalibrated image without feature point correspondences. Experimental results on synthetic and real images are presented to show its performance.
Cue Validity and Object-Based Attention
Authors: Xun He
Venue: Journal of Cognitive Neuroscience (2004)

In a previous study, Egly, Driver, and Rafal (1994) observed both space- and object-based components of visual selective attention. However, the mechanisms underlying these two components and the relationship between them are not well understood. In the present research, with a similar paradigm, these issues were addressed by manipulating cue validity. Behavioral results indicated the presence of both space- and object-based components under high cue validity, similar to the results of Egly et al.'s study. In addition, under low cue validity, the space-based component was absent, whereas the object-based component was maintained. Further event-related potential results demonstrated an object-based effect at a sensory level over the posterior areas of brain, and a space-based effect over the anterior region. The present data suggest that the space- and object-based components reflect mainly voluntary and reflexive mechanisms, respectively.
Print-Based Digital Content Interfaces for Social Reading Activities
Authors: Deborah Turk, John Brine, Kamen Kanev
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This paper investigates the application of digitallyenhanced print-based interfaces to support a combined individual and social jigsaw reading activity. The project team aimed to design and develop an activity that required students to read and process complex authentic technical texts. Informed by student feedback, this research project evaluated the benefits of enhancing the activity with a patented Cluster Pattern Interface (CLUSPI®) [1] to facilitate the students' reading comprehension and improve their multiliteracy skills. CLUSPI® is a cluster pattern-based encoding scheme that allows digitally encoded information to be embedded in a layer of clustered graphical objects on printed documents. CLUSPI® can expand the application of multimedia by linking paperbased materials to language learning tools through a point-and-click interface.
HelioRoom: problem-solving in a whole class visual simulation
Authors: Mark Thompson, Tom Moher
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

HelioRoom simulates the orbital motion of the planets in the Solar system on a set of synchronized computer displays situated on the walls of the classroom, with planets "orbiting" around the periphery of the room. Third grade students used the application for a problem-solving activity requiring the collection of evidence in support of theories concerning the identity of planets whose size and surface characteristics were intentionally obscured.
A CFAR based model order selection criterion for complex sinusoids
Authors: Changzheng Ma, Boon Poh Ng
Venue: Signal Processing (2006)

The model order selection problem for complex sinusoidal signals has often been tackled using Akaike information criterion (AIC), minimum description length principle (MDL) or maximum a posterior criterion (MAP). As a finite-length noise sequence (discrete-time noise signal) can be expressed as a summation of sinusoidal signals, we propose signal-energy-to-noise-power-ratio (SENR) to be considered in the model order selection problem. In our proposal, a predefined SENR threshold is used to decide whether a signal exists or not and the signal order can then be obtained. Since this new criterion is just like the constant false alarm rate (CFAR) detector, we call the new criterion the CFAR criterion. In this paper, the performances of the AIC, MDL, MAP and CFAR criteria are compared. Simulation results testify the correctness of our analysis and the superior performance of the CFAR criterion.
Global-to-Local Histogram Match Culling for Epipolar Geometry Estimation
Authors: Murray Evans, James Ferryman
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

Estimation of the Epipolar Geometry and Fundamental Matrix for a pair of stereo images often begins with a stage of feature (corner) detection and correlation based feature matching. This generally results in a set of matches where features are matched multiple times and frequently incorrectly. This paper introduces a simple and fast histogram based technique for refining the set of matches prior to a final stage of robust Fundamental Matrix estimation that compares favourably to previously published techniques such as SVD and Relaxation.
Individual Tariffs for Mobile Services: Theoretical Framework and a Computational Case in Mobile Music
Authors: Hong Chen, Louis-Francois Pau
Venue: Proceedings of the International Conference on Mobile Business (2006)

This paper introduces individual tariffs at service and content bundle level in mobile communications. It gives a theoretical framework (economic, sociological) as well as a computational game solution method. The user can be an individual or a community. Individual tariffs are decided through interactions between the user and the supplier. A numerical example from mobile music illustrates the concepts.
Selling PC Software: A Once-Irresistible Opportunity
Authors: Seymour Merrin
Venue: IEEE Annals of the History of Computing (2006)

How--and why--does an ex-geologist and semiconductor executive leave the corporate world and instead make a leap into microcomputer software sales? This memoir tells the story of a risk-taker who saw an unparalleled opportunity and took it.
Implementation and Deployment Process of IMS Learning Design: Findings from the Canadian IDLD Research Project
Authors: Karin Lundgren-Cayrol, Olga Marino, Gilbert Paquette, Michel Leonard, Ileana de la Teja
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

IDLD, Implementation and Deployment of Learning Design http://www.idld.org, is a Canadian project that aims to bridge the gap among IMS Learning Design researchers and developers and pedagogical designers. Two main products of the project are a well founded, well supported and well tooled implementation process for describing new or existing IMS-LD compliant units of learning and a repository of reusable Learning Design examples and generic templates. In this paper we describe the proposed implementation process and tools and how it was carried out by four IDLD teams building Units of Learning (UoL) for the repository. In each case, IMSLD compliant Units of Learning were produced from existing scenarios, leading to a set of both generic and exemplary UoLs. The scenarios were carefully chosen to assure some design quality in order to enhance the reusability potential. The paper concludes with some recommendations and next steps.
Sub-optimal all-sky detection of periodic gravitational waves
Authors: Stefano Marano, Vincenzo Matta
Venue: Signal Processing (2006)

The design of a (roughly speaking optimal) GLRT-based detector for continuous gravitational waves (GWs) is a simple task and results in a bank-of-filter structure. Unfortunately, the computational burden of such a GLRT is unaffordable with the current computational powers: resorting to some kind of sub-optimal detectors seems unavoidable. We propose three new detection algorithms that can be implemented with a reasonable computational complexity, and analyze their behaviors both in terms of error probabilities and in terms of computational costs. With respect to these performance figures the improvements over the classical GLRT are impressive.