ppxscal/embedding_network huggingface dataset:
Multiple item capacitated random yield systems
Authors: Scott E. Grasman
Venue: Computers and Industrial Engineering (2009)

Two main contributors to the uncertainty of production systems are capacity and random yield; therefore, strategies are needed that incorporate both random yield and the increased effect due to capacity restrictions. This paper utilizes dynamic programming and linear programming transformation to provides a method to specify an optimal decision for a given inventory state. In doing so, it can be empirically shown that the structure of the optimal policy is not an order-up-to policy. Using this method, one only needs to solve a small subproblem, which using LP transformation can be done using commercially available LP solvers without concern for solution time. These results may improve the decision-making capabilities of real-time complex environments since the emphasis is on developing policy rules that are easy to implement in manufacturing applications, as well as service industries such as airlines, healthcare and education. Additionally, the results may be used as a means of evaluating (bounding) existing approximation methods.
Dynamic Data Driven Applications Systems --- DDDAS 2009
Authors: Craig C. Douglas
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

This workshop covers several aspects of the Dynamic Data Driven Applications Systems (DDDAS) concept, which is an established approach defining a symbiotic relation between an application and sensor based measurement systems. Applications can accept and respond dynamically to new data injected into the executing application. In addition, applications can dynamically control the measurement processes. The synergistic feedback control-loop between an application simulation and its measurements opens new capabilities in simulations, e.g., the creation of applications with new and enhanced analysis and prediction capabilities, greater accuracy, longer simulations between restarts, and enable a new methodology for more efficient and effective measurements. DDDAS transforms the way science and engineering are done with a major impact in the way many functions in our society are conducted, e.g., manufacturing, commerce, transportation, hazard prediction and management, and medicine. The workshop will present such new opportunities as well as the challenges and approaches in technology needed to enable DDDAS capabilities in applications, relevant algorithms, and software systems. The workshop will showcase ongoing research in these aspects with examples from several important application areas.
On the remainder term for analytic functions of Gauss quadratures with double fixed nodes
Authors: Daniel Vladislav
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

We study the kernels in the contour integral representation of the remainder term of Gauss-Lobatto quadratures, in particular the location of their maxim on circular and elliptic contours. Quadrature rules with Chebyshev weight functions of all four kinds receive special attention. We also study a general Gauss-Chebyshev-Stancu quadrature with double fixed nodes.
Computationally efficient underwater acoustic 2-D source localization with arbitrarily spaced vector hydrophones at unknown locations using the propagator method
Authors: Jin He, Zhong Liu
Venue: Multidimensional Systems and Signal Processing (2009)

This paper presents a propagator-based algorithm for underwater acoustic 2-D direction-of-arrival (DOA) estimation with arbitrarily spaced vector hydrophones at unknown locations. The proposed algorithm requires only linear operations but no eigen-decomposition or singular value decomposition into the signal and noise subspaces. Comparing with its ESPRIT counterpart (Wong and Zoltowski, IEEE J Oceanic Eng 22:566---575, 1997a), the proposed propagator algorithm has its computational complexity reduced by this ratio: the number of sources to quadruple the number of vector hydrophones. Simulation results show that at high and medium signal-to-noise ratio, the proposed propagator algorithm's estimation accuracy is similar to its ESPRIT counterpart.
International Workshop on Context-Oriented Programming
Venue: European Conference on Object-Oriented Programming (2009)

Context information plays an increasingly important role in our information centric world. Software systems must adapt to changing contexts over time, and must change even while they are running. Unfortunately, mainstream programming languages and development environments do not support this kind of dynamic change very well, leading developers to implement complex designs to anticipate various dimensions of variability. Context-oriented Programming (COP) directly supports variability depending on a wide range of dynamic attributes, making it possible to dispatch run-time behavior on any properties of the execution context. By now, several researchers have started to work on Context-oriented Programming and related ideas, and first implementations ranging from first prototypes to mature platform extensions used in commercial deployments have illustrated how multi-dimensional dispatch can indeed be supported effectively to achieve expressive run-time variation in behavior. We received 11 submissions. Each paper was reviewed by 3 members of our program committee. 10 papers were selected for presentation and publication in the proceedings of the workshop.
Discovering potential musical instruments teachers using data clustering approach
Authors: Cheng-Fa Tsai, Yu-Tai Su, Chiu-Yen Tsai, Chun-Yi Sung
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

This paper aims to introduce data clustering approach to discover potential musical instruments teachers. With a total of 5125 candidates registered respectively in 9 grades during 2000-2008. Moreover, this work presents a new data clustering algorithm named MIDBSCAN and an existing well-known neural network called self-organizing map (SOM) to perform data clustering applications for finding potential musical instruments teachers. According to our simulation results, the proposed MIDBSCAN approach has low execution time cost, a maximum deviation in clustering correctness rate and a maximum deviation in noise data filtering rate.
Local search of orthogonal packings using the lower bounds
Authors: V. M. Kartak, M. A. Mesyagutov, E. A. Mukhacheva, A. S. Filippova
Venue: Automation and Remote Control (2009)

Consideration was given to the problem of packing the rectangular half-infinite strip. It was suggested to solve it using a one-point evolutionary algorithm with search of the best solution in the neighborhood of the local lower bound. Proposed was an algorithm to construct this neighborhood including the packings obtained by solving a special problem of one-dimensional cutting. Its solution was shown to be the local lower bound in the considered neighborhood. An improved global lower bound was proposed. The results of numerical modeling were presented. The record value obtained was compared with the global bounds.
Compact Representation of Sets of Binary Constraints
Authors: Jussi Rintanen
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

We address the problem of representing big sets of binary constraints compactly. Binary constraints in the form of 2-literal clauses are ubiquitous in propositional formulae that represent real-world problems ranging from model-checking problems in computer-aided verification to AI planning problems. Current satisfiability and constraint solvers are applicable to very big problems, and in some cases the physical size of the problem representations prevents solving the problems, not their computational difficulty. Our work is motivated by this observation. We propose graph-theoretic techniques based on cliques and bicliques for compactly representing big sets of binary constraints that have the form of 2-literal clauses. An n, m biclique in a graph associated with the constraints can be very compactly represented with only n+m binary constraints and one auxiliary variable. Cliques in the graph are associated with at-most-one constraints, and can be represented with a logarithmic number of binary constraints. The clique representation turns out to be a special case of the biclique representation. We demonstrate the effectiveness of the biclique representation in making the representation of big planning problems practical.
Analytic Comparison of Self-Organising Maps
Authors: Rudolf Mayer, Robert Neumayer, Doris Baum, Andreas Rauber
Venue: Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (2009)

SOMs have proven to be a very powerful tool for data analysis. However, comparing multiple SOMs trained on the same data set using different parameters or initialisations is still a difficult task. In most cases it is performed only via visual inspection or by utilising one of a range of quality measures to compare vector quantisation or topology preservation characteristics of the maps. Yet, comparing SOMs systematically is both necessary as well as a powerful tool to further analyse data: necessary, because it may help to pick the most suitable SOM out of different training runs; a powerful tool because it allows analysing mapping stabilities across a range of parameter settings. In this paper we present an analytic approach to compare multiple SOMs trained on the same data set. Analysis of output space mapping, supported by a set of visualisations, reveals data co-locations and shifts on pairs of SOMs, considering both different neighbourhood sizes at source and target maps. A similar concept of mutual distances and relationships can be analysed at a cluster level. Finally, Comparisons aggregated automatically across several SOMs are strong indicators for strength and stability of mappings.
Polarization Radar HRRP Recognition Based on Kernel Methods
Authors: Liya Li, Hongwei Liu, Bo Jiu, Shunjun Wu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Although the multi-polarized high resolution range profiles (HRRP) include more target information than single-polarized HRRP, the recognition becomes more difficult because of the huge data and the complex data distribution. Kernel methods based on the multi-polarized HRRPs are proposed in this paper. Two kernel functions based on the multi-polarized HRRPs are first proposed, and then they are employed to the kernel principal component analysis (KPCA) respectively. Finally, the nearest neighbor (1NN) classifier and the support vector machine (SVM) classifier are used to identify the unknown targets. Experimental results based on the simulated multi-polarized HRRPs data show that the proposed methods can raise the correct recognition rate greatly compared with the single-polarized HRRP recognition. Moreover, the computational complexity can be decreased and the recognition performance can be increased to some extent compared with the methods of combination of the single-polarized classifiers.
Comparison of data models for plant lifecycle information management
Authors: Pekka Siltanen, Antti Pärnänen
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Several data models have been defined for enabling information interoperability during the process plant life cycle. The data models differ in details and their scopes are different, making it difficult to compare and evaluate them. While the importance of using commonly agreed standard data models has widely been accepted, the diversity of the models hinders companies from choosing between them when implementing e.g. data warehouses. In many cases this leads to implementing in-house, non-standard models. In this paper, criteria for evaluating and comparing data models are introduced. Five data models developed by international or national organizations are introduced and compared, based on the criteria.
Tactile displays for multitask environments: The role of concurrent task processing code
Authors: Thomas Ferris, Shameem Hameed, Nadine Sarter
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

The distribution of tasks and stimuli across multiple modalities has been proposed as a means to support multitasking in data-rich environments. In particular, the tactile channel and, more specifically, communication via the use of tactile/haptic icons have received considerable interest in recent years. Past research has examined primarily the impact of concurrent task modality on the effectiveness of tactile information presentation. In contrast, the present study investigates to what extent the interpretation of complex tactile patterns - “tactons” - is affected by another attribute of information: the processing code of concurrent tasks. Participants decoded tactons composed of temporal patterns of vibrations (categorical data) - and concurrently interpreted one of two types of visual task stimuli - requiring either spatial or categorical processing - in a driving simulation. Compared to single-task performance, both dual-task conditions showed a performance decrement. As predicted by Multiple Resource Theory, this decrement was significantly larger when the tacton task was paired with the visual task requiring categorical (as compared to spatial) processing. The findings from this study can serve as input to multidimensional quantitative models of timesharing performance. From an applied perspective, the results suggest that nonspatially-encoded tactons would be preferable in environments which rely heavily on spatial processing, such as car cockpits or flight decks.
Deformation field analysis of magnetic resonance images in Alzheimer's disease
Authors: B. S. Mahanand, M. Aswatha Kumar
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

Medical imaging has gone through a revolution since the advent of X-ray computed tomography imaging in early seventies and the introduction of other imaging modalities such as magnetic resonance imaging, positron emission tomography, etc in the later years. Image registration plays a vital role in current clinical practice and biomedical research. The main aim of the paper is to investigate and analyze brain tissue changes in Alzheimer's disease using non-rigid medical image registration and statistical analysis techniques. In our proposed approach, first the source and the target images are affinely registered to correct global differences between the images and then non-rigid registration based on free-form deformation using B-spline approximation is performed on the images. The resulting displacement values from the non-rigid registration are further investigated for deformation in the entire brain to detect typical deformation patterns. Finally, statistical method namely t-test is performed for analysis of the results. The initial results indicate that the tissue volume change in the brain occurs predominantly in the hippocampus of the brain.
Extracting Core ICT Indicators Using Entropy Method
Authors: Payam Hanafizadeh, Mohammad Reza Hanafizadeh, Mohsen Khodabakhshi
Venue: The Information Society (2009)

In this article a methodology is presented to extract indicators that appropriately measure the information society and the digital divide between countries and the relevant statistics that the majority of countries can collect. With the help of content analysis, the entropy method, and consideration of the diffusion aspects of digitalization, 37 reputable information society and digital divide models are analyzed to indentify “core information and communication technology (ICT) indicators.” To overcome the limitation of the nonexistence of data, the information and the knowledge embedded in information society and digital divide models are employed as proxies for experts' opinions for extracting the core ICT indicators. Comparison of the prior indicators and the proposed ones reveals that the former ignore three important dimensions: e-learning, e-government, and networked world enablers.
An energy scalable computational array for energy harvesting sensors
Authors: Raj Amirtharajah
Venue:  (2008)

Harvesting energy from environmental sources can extend the lifetime of wireless sensor network nodes beyond the limits of battery technology. However, the output power from an energy harvester is highly variable. This dissertation describes a domain-specific computational array which maximizes sensor availability by matching system power consumption to the obtainable scavenged energy through power scalable approximate signal processing. The array consists of distributed arithmetic (DA)-based functional units coupled with an island-style reconfigurable interconnection network. Each functional unit is capable of computing a set of linear and nonlinear core signal processing functions in an area efficient manner, which also minimizes leakage power. Sensor DSP applications, such as Finite-Impulse Response (FIR) filters, Fast Fourier Transform (FFT), rectangular-to-polar conversion, polar-to-rectangular conversion, and Taylor polynomial evaluation, are mapped onto the array through the reconfigurable interconnect structure. A low power sensor DSP chip of a 4x4 array has been designed and implemented to validate our architecture concepts and circuit techniques. The chip was fabricated in 0.25&mu;m CMOS technology.
Partitioning graphs of supply and demand
Authors: Takehiro Ito, Xiao Zhou, Takao Nishizeki
Venue: Discrete Applied Mathematics (2009)

Assume that each vertex of a graph G is either a supply vertex or a demand vertex and is assigned a positive integer, called a supply or a demand. Each demand vertex can receive ''power'' from at most one supply vertex through edges in G. One thus wishes to partition G into connected components by deleting edges from G so that each component C has exactly one supply vertex whose supply is no less than the sum of demands of all demand vertices in C. If G does not have such a partition, one wishes to partition G into connected components so that each component C either has no supply vertex or has exactly one supply vertex whose supply is no less than the sum of demands in C, and wishes to maximize the sum of demands in all components with supply vertices. We deal with such a maximization problem, which is NP-hard even for trees and strongly NP-hard for general graphs. In this paper, we show that the problem can be solved in pseudo-polynomial time for series-parallel graphs and partial k-trees-that is, graphs with bounded tree-width.
Residual of Resonant SVD as Salient Feature
Authors: Dmitry Chetverikov
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

Computer vision approaches to saliency are based, among others, on uniqueness [1], local complexity [2], distinctiveness [3,4], spectral variation [5], and irregularity [6]. Saliency can also be viewed as the information in the data relative to a representation or model [7]. When a representation is built, a residual error is often minimised. The residual can be used to obtain saliency maps for solving challenging tasks of image and video processing. We introduce the notion of the resonant SVD and demonstrate that the SVD residual at the resonant spacing is selective to defects in spatially periodic surface textures and events in time-periodic videos. Examples with real-world images and videos are shown and discussed.
Calculation of diffusion process nonlinearity according to mass measurements
Authors: Marija Iltina, Ilmars Iltins
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

There is an approach of calculating diffusion process nonlinearity, by applying changes of measured diffusing mass. Discussed approach is based on the fact that in the event of nonlinear process sorption and desorption occur at different speeds. Rate of diffusion process nonlinearity is determined according to measurements of time that is required for mass of diffusing substance to achieve a given value in sorption and desorption process.
Financial Forecasting Using Character N-Gram Analysis and Readability Scores of Annual Reports
Authors: Matthew Butler, Vlado Kešelj
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Two novel Natural Language Processing (NLP) classification techniques are applied to the analysis of corporate annual reports in the task of financial forecasting. The hypothesis is that textual content of annual reports contain vital information for assessing the performance of the stock over the next year. The first method is based on character n-gram profiles, which are generated for each annual report, and then labeled based on the CNG classification. The second method draws on a more traditional approach, where readability scores are combined with performance inputs and then supplied to a support vector machine (SVM) for classification. Both methods consistently outperformed a benchmark portfolio, and their combination proved to be even more effective and efficient as the combined models yielded the highest returns with the fewest trades.
Bayesian modelling of colour's usage impact to web credibility
Authors: Eleftherios Papachristos, Nikolaos Tselios, Nikolaos Avouris
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Colour plays an important role in web site design. The selection of effective chromatic combinations and the relation of colour to the perceived aesthetic and emotional value of a web site is the focus of this paper. The subject of the reported research has been to define a model through which to be able to associate colour combinations with specific desirable emotional and aesthetic values. The presented approach involves application of machine learning techniques on a rich data set collected during a number of empirical studies. The data obtained were used to train a Bayesian Belief Network which associated a simple chromatic model to perceived aesthetic and emotional dimensions. A set of tools that have been build in the process to support the methodological framework and ensure its reusability with minimal effort, are also described.
SPSS Demystified: A Step-by-Step Guide to Successful Data Analysis, 1st edition
Authors: Ronald D. Yockey
Venue:  (2007)

For courses in Statistics and Research Methods. Without question, statistics is one of the mostchallenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students anxiety with statistics (and the fact that this anxiety can affect performance),Ronald Yockey has written SPSS Demystified: A Step-by -Step Guide to Successful Data Analysis, 1/e. Through a comprehensive, step-by-step approach, this textisconsistently andspecifically designed toboth alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Through a comprehensive, step-by-step approach, this textisconsistently andspecifically designed toboth alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. 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