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Logical classification of distributed algorithms (Bakery algorithms as an example)

THEORETICAL COMPUTER SCIENCE 2011年第25期412卷 2724-2745页

作者： Abraham, Uri Ben Gurion Univ Negev Dept Math & Comp Sci IL-84105 Beer Sheva Israel

We argue that logical descriptions of distributed algorithms can reveal key features of their high-level properties, and can serve to classify and explicate fundamental similarities even among superficially very dissimilar algorithms. As an illustration, we discuss two distinct mutual-exclusion algorithms: the Bakery algorithm of Lamport is for shared memory, and the Ricart and Agrawala version is for message passing. It is universally agreed that they are both instances of "the Bakery algorithm" family, but is there a formal expression of this affinity? Here we present logical properties expressed naturally in Tarskian event structures that allow us to capture the similarities precisely. We use the notions of low-level and high-level events to organize the comparison. We find a set of properties expressed in quantification language which are satisfied by every Tarskian system execution that models a run by either one of the protocols, and we suggest these properties as a formal explication for the similarity of the two algorithms. An abstract proof shows that these common properties imply the mutual exclusion, and the informal arguments explain the sense in which they capture the essence of the two Bakery algorithms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Tarski structures classification of algorithms Event structures Mutual exclusion Distributed algorithms Models of concurrency Many-sorted logics

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PARTIAL VOLUME TISSUE classification OF MULTICHANNEL MAGNETIC-RESONANCE IMAGES - A MIXEL MODEL

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1991年第3期10卷 395-407页

作者： CHOI, HS HAYNOR, DR KIM, YM UNIV WASHINGTON DEPT RADIOLSEATTLEWA 98195

A single volume element (voxel) in a medical image may be composed of a mixture of multiple tissue types. We call voxels which contain multiple tissue classes mixels. A statistical mixel image model based on Markov random field (MRF) theory and an algorithm for the classification of mixels are presented in this paper. We concentrate on the classification of multichannel magnetic resonance (MR) images of the brain although the algorithm has other applications. We also present a method for compensating for the gray-level variation of MR images between different slices, which is primarily caused by the inhomogeneity of the RF field produced by the imaging coli.

关键词： classification of algorithms RF field pixel Inhomogeneities algorithms Writers Graphical models Medical imaging Markov random fields Mixel

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An Efficient and Effective Robust Algorithm for the classification of Jakarta Vegetation Area

An Efficient and Effective Robust Algorithm for the Classifi...

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5th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Herwindiati, Dyah E. Isa, Sani M. Arisandi, Desi Tarumanagara Univ Informat Technol Fac Jakarta Indonesia

ISBN: (纸本)9789791421195

This paper discusses an efficient and effective robust algorithm applied to the classification of vegetation areas in the Jakarta Province. The input data is remote sensing data from the Landsat 7 Satellite. The classification process is guided over two steps, training and classification. The purpose of the training step is to determine the reference spectra of the vegetation area, and the purpose of the classification step is to classify Jakarta areas as either vegetation or nonvegetation. An efficient robust approach is used to classify the Jakarta area using the anomolous digital number resulting from a failed instrument. This paper discusses the application of an efficient and effective robust method to classify the remote sensing data with anomolous or inconsistent observations. The aim is to propose a new efficient subset robust approach-the subset minimum vector variance-to classify the vegetation area of Jakarta. The minimum vector variance (MVV) is a robust method having a minimum of the square of the length of a parallelotope diagonal.

关键词： remote-sensing data vegetation classification of algorithms REFERENCE SPECTRUM Robustness incoming data Taxonomy

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Distributed boosting algorithm for classification of text documents

Distributed boosting algorithm for classification of text do...

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IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sarnovsky, Martin Vronc, Michal Tech Univ Kosice Fac Elect Engn & Informat Dept Cybernet & Artificial Intelligence Kosice 04200 Slovakia

ISBN: (纸本)9781479934423

Presented paper focuses on the area of analysis and classification of textual documents. We present the classification of documents based on boosting method applied on the decision tree algorithm. Main objective of the paper is to present the implementation of distributed boosting algorithm based on Map Reduce paradigm. We have used the GridGain framework as a platform for distributed data processing and have tested the implemented solution on two different dataset within our testing environment.

关键词： classification of algorithms DISTRIBUTED DATA PROCESSING Boost TEST ENVIRONMENT text documents Decision Trees Taxonomy Distribute MAPPING(MATHEMATICAL) Area Analysis

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Freight demand statistical modeling: A classification and review

Freight demand statistical modeling: A classification and re...

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46th Annual Transportation Research Forum 2005

作者： Boile, M.P. Golias, M. Dept. of Civil and Environmental Engineering Rutgers University NJ United States

ISBN: (纸本)9781618394088

Linear Regression is used as a prediction tool in transportation planning, traffic data analysis and safety. Many researchers have attempted to address several transportation issues using these types of models. The accuracy and stability of these models is mainly dependent on the size of the available data. Unlike other science fields, where sophisticated algorithms are used to deal with problems of small datasets, the majority of freight demand modeling relies on simple statistical techniques. Limitations of these modeling methodologies, caused by their high dependence on data availability, and several assumptions that need to be made can result in erroneous models. In cases in which limited data are available, more advanced algorithms that can be legitimately used on small datasets should be applied. In this paper a description and classification of algorithms and processes used for creating these types of models under limited data is presented. To demonstrate the applicability of these algorithms, along with implementation problems, limitations, and the different performance measures, a case study is used. Different models are created and results are presented and discussed. © (2005) by the Transportation Research Forum All rights reserved.

关键词： cargo algorithms qualified data classification of algorithms statistical models (nuclear) data availability Linear Regression Taxonomy Dataset

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PARALLEL APPLICATION OF BLOCK-ITERATIVE METHODS IN MEDICAL IMAGING AND RADIATION-THERAPY

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MATHEMATICAL PROGRAMMING 1988年第2期42卷 307-325页

作者： CENSOR, Y HOSP UNIV PENN DEPT RADIOLMED IMAGE PROC GRPPHILADELPHIAPA 19104

Some row-action algorithms which exploit special objective function and constraints structure have proven advantageous for solving huge and sparse feasibility or optimization problems. Recently developed block-iterative versions of such special-purpose methods enable parallel computation when the underlying problem is appropriately decomposed. This opens the door for parallel computation in image reconstruction problems of computerized tomography and in the inverse problem of radiation therapy treatment planning, all in their fully discretized modelling approach. Since there is more than one way of deriving block-iterative versions of any row-action method, the choice has to be made with reference to the underlying real-world problem.

关键词： Full discretization computerized tomography image reconstruction radiotherapy treatment planning block-iterative algorithms parallel computations Cimmino's algorithm entropy maximization classification of algorithms

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The Review of Target Tracking for UAV 14

The Review of Target Tracking for UAV

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chen, Peng Zhou, Yimin Nanchang Univ Sch Informat Engn Nanchang 330038 Jiangxi Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781538694909

The target tracking based on UAV can effectively accomplish all kinds of tasks with the aid of vision processing system. In this paper, we focus on the single-target tracking, where the first frame of the rectangular calibration of the target is captured and used for target tracking in the subsequent frames. First, the visual tracking algorithms are briefly introduced, and the UAV based visual tracking algorithms are divided into generative and discriminative categories. The advantages and disadvantages of the target tracking methods based on UAV is summarized. Furthermore, the application of target tracking algorithms based on UAV are discussed and the developmental prospects for target tracking based on UAV are also provided.

关键词： Target tracking classification of algorithms UAV

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On a Mathematical Framework for Object Recognition from Multi-Perspective Remotely Sensed Imagery

On a Mathematical Framework for Object Recognition from Mult...

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IEEE SoutheastCon 2011 - Building Global Engineers

作者： Thomas, Alan M. Burkhart, J. Michael Nichols, C. Spencer Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30332 USA Georgia Inst Technol Sch Mat Atlanta GA 30332 USA Georgia Inst Technol Sch Phys Atlanta GA 30332 USA

ISBN: (纸本)9781612847382

We develop a new perspective invariant feature space representation of remotely sensed objects, regarding the features themselves as primitive observables of the 3D objects and to estimate them from multiple sensor measurements. This is formulated as an inverse problem in the feature coefficients. Once the coefficients are estimated they may be used to derive higher level features used by machine learning algorithms for classification. The focus of this paper is on the mathematical formulation of the feature estimation problem from one or more perspective images. We also give a discussion of how this fits into a larger object classification system.

关键词： object recognition images Machine learning algorithms three dimensional shape Imagery (Psychotherapy) Inverse method Perspective classification of algorithms Eigenvector Frameworks

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Rainfall Presage Forecast Using Machine Learning Approach

Rainfall Presage Forecast Using Machine Learning Approach

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sona, Deepika Rani Das, Rashmi Ranjan Vikas Reddy, S. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Rainfall prediction is vital for various applications such as agriculture, flood control, and water resource management. Accurate rainfall prediction can help farmers to make decisions about irrigation and crop selection. It can also help authorities to take necessary measures to prevent or mitigate the impact of floods. In the proposed study, multiple algorithms are examined and compared in terms of how accurately they predict rainfall. The accuracy of rainfall forecasts may be increased by using machine learning algorithms, which have shown considerable promise in predicting rainfall patterns. The authors solely used the AUC score and categorial standards to analyze six ML algorithms. Based on their accuracy score, AUC, precision, recall, and f1 score, authors compared algorithms including Random Forest, K-Nearest Neighbours (KNN), Logistic Regression, Cat Boost, Naive Bayes, and Gradient Boost. Since they provide a useful solution for categorization and prediction of the amount of rainfall as well as precise rate prediction, the applied algorithms are supervised learning algorithms. © 2023 IEEE.

关键词： classification of algorithms F1 score KNN Machine learning Rainfall

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classification Based on Neuroimaging Data by Tensor Boosting

Classification Based on Neuroimaging Data by Tensor Boosting

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International Joint Conference on Neural Networks

作者： Bo Zhang Hua Zhou Liwei Wang Chul Sung IBM University of California Pharmaceutical Product Development Inc.

ISBN: (纸本)9781509061839

Recent advances in medical imaging technologies generate a high volume of imaging data. classification of cognitive outcome and disease status based on brain images is one of the most important tasks in neuroimaging studies. However it poses great challenge to the current classification methods due to the extremely high dimensionality and low signal to noise ratio in brain image data. In this article we propose a tensor boosting algorithm for classification based on neuroimaging data. The method is off-the-shelf, computationally simple and amenable to various modalities of neuroimaging data. The method is applied to an EEG data set from an alcoholism study and an MRI data set from an ADHD Global Competition and shows significantly improved classification performance.

关键词： low SNR Neuroimaging Taxonomy classification Methods signal to noise ratios image data Tensor Boost HIGH CAPACITY distress level Imaging classification of algorithms

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