ppxscal/embedding_network huggingface dataset:
Nontological Engineering
Authors: Wacław Kuśnierczyk
Venue: Proceeding of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006) (2006)

This article reflects an ongoing effort to systematize the use of terms applied by philosophers and computer scientists in the context of ontology and ontological engineering. We show that a common reference terminology is needed to connect terms in representational artifacts to what they mean ontologically. Without such a reference, statements in and about knowledge representation languages will be ambiguous, both as between various languages and within a single language. We identify problems common to a number of knowledge representation languages used to formalize ontologies. We show that a reference terminology can be used to disambiguate the meanings of some, and to reveal ontological problems in other, evidently confused, statements in and about different representation languages. Our final conclusion is not that our proposed terminology is the ultimate one to serve as a common reference; rather, we argue that it is necessary to have such a standard with well-defined terms linked to an axiomatized theory, if unambiguous cross-paradigm and cross-language communication is to be achieved.
Traffic Condition Recognition of Probability Neural Network Based on Floating Car Data
Authors: Gengqi Guo, Chengtao Cao, Jiuzhong Li, Shuo Shi
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

A traffic condition recognition method based on floating car data was proposed by analyzing Probability Neural Network (PNN) and Global K-means algorithm. The related factors of traffic condition and the collection method of floating car data were presented. A probability neural network classifier was designed using Global K-means algorithm and applied to the recognition of traffic condition with floating car data. The experiment results showed that the method could recognize traffic condition well. The accurate rate is satisfactory.
Counterparts in Language and SpaceSimilarity and S-Connection
Authors: Joana Hois, Oliver Kutz
Venue: Proceeding of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008) (2008)

We aim to combine the semantics of spatial natural language specified as a linguistically motivated ontology, the Generalized Upper Model, with spatial logics or ontologies that specify space according to certain conceptualisations, based on regions, shapes, orientations, distances, or object properties. Such combinations, however, introduce uncertainties of various kinds, caused by different levels of detail in the definition of one of the spatial ontologies, underspecifications within parts of an ontology, or different viewpoints of the topics the ontologies address. To model these problems formally, we extend the combination technique of E-connections by adding (heterogeneous) similarity measures. Local similarity compares objects within one domain, whilst comparing objects across domains leads to similarity measures that are motivated by and based on counterpart-theoretic semantics. The new formalism is called S-connection.
A Stochastic Complex Dynamical Network and Its Synchronization
Authors: Tong-Jun He, Zhengping Shi
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this brief, the authors firstly introduce a complex stochastic dynamical network model. Secondly, the existence and uniqueness of solution of this stochastic complex dynamical network is identified, furthermore, the authors investigate this stochastic complex dynamical network's synchronization in probability and give out two synchronization theorems of this network. Two detail examples are given to verify the theoretical analysis, and numerical simulations verify that the two synchronization theorems are effective.
Effective spatial clustering methods for optimal facility establishment
Authors: Ashkan Zarnani, Masoud Rahgozar, Caro Lucas, Fattaneh Taghiyareh
Venue: Intelligent Data Analysis (2009)

Selecting optimal locations for new facilities is a critical decision in organizations that provide field-based services such as delivery, maintenance and emergency services. The total logistics cost and facility establishment cost are the main objectives of the location selection procedure. With the increasing size of this problem in today's applications, the aspects of efficiency and scalability have developed into major challenges. In this paper, we study the use of spatial clustering methods to solve this problem and propose two new algorithms. The new algorithms determine the optimal locations of the new facilities plus their optimal total count during the search process. We have conducted many experiments for empirical comparative study on the application of several spatial clustering algorithms for optimal facility establishment. The benchmarks are conducted with both real world and synthetic data sets. The results reveal advantages of the proposed algorithms and confirm that these algorithms have better performance in terms of efficiency and objectives in the field-based services. Hence, the higher scalability and effectiveness of the proposed algorithms make them suitable solutions for the problem of optimal facility establishment with large databases.
Multi-instance learning by treating instances as non-I.I.D. samples
Authors: Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances. In this paper, we propose two simple yet effective methods. In the first method, we explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags. In the second method, we implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information. The effectiveness of the proposed methods are validated by experiments.
Process Engineering and Design Using Visual Basic
Authors: Arun K. Datta
Venue:  (2007)

Software tools are a great aid to process engineers, but too much dependence on such tools can often lead to inappropriate and suboptimal designs. Reliance on software is also a hindrance without a firm understanding of the principles underlying its operation, since users are still responsible for devising the design. In Process Engineering and Design Using Visual Basic, Arun K. Datta provides a unique and versatile suite of programs along with simultaneous development of the underlying concepts, principles, and mathematics. Each chapter details the theory and techniques that provide the basis for design and engineering software and then showcases the development and utility of programs developed using the material outlined in the chapter. This all-inclusive guide works systematically from basic mathematics to fluid mechanics, separators, overpressure protection, and glycol dehydration, providing basic design guidelines based on international codes. Worked examples demonstrate the utility of each program, while the author also explains problems and limitations associated with the simulations. After reading this book you will be able to immediately put these programs into action and have total confidence in the result, regardless of your level of experience. All nine programs are available on the companion CD-ROM, including a useful unit conversion tool. 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
Self-stabilization preserving compiler
Authors: Shlomi Dolev, Yinnon Haviv, Mooly Sagiv
Venue: ACM Transactions on Programming Languages and Systems (TOPLAS) (2009)

Self-stabilization is an elegant approach for designing fault tolerant systems. A system is considered self-stabilizing if, starting in any state, it converges to the desired behavior. Self-stabilizing algorithms were designed for solving fundamental distributed tasks, such as leader election, token circulation and communication network protocols. The algorithms were expressed using guarded commands or pseudo-code. The realization of these algorithms requires the existence of a (self-stabilizing) infrastructure such as a self-stabilizing microprocessor and a self-stabilizing operating system for their execution. Moreover, the high-level description of the algorithms needs to be converted into machine language of the microprocessor. In this article, we present our design for a self-stabilization preserving compiler. The compiler we designed and implemented transforms programs written in a language similar to the abstract state machine (ASM). The compiler preserves the stabilization property of the high level program.
Bayesian learning framework with kernel-imbedded gaussian processes applied to microarray analysis
Authors: Guylaine Poisson
Venue:  (2008)

DNA microarray technology has provided researchers a high-throughput means to simultaneously measure expression levels for thousands of genes in an experiment. With a probit regression setting and assuming that the link function between significant gene expression data and latent variable for the response label is a Gaussian process, a kernel-induced hierarchical Bayesian framework is built for a cancer classification problem by using microarray gene expression data. Targeting a multi-classification problem and adopting a variable selection approach with a Gibbs sample as core, we developed the algorithm, kernel-imbedded Gaussian Process (KIGP), to analyze microarray data under a Bayesian framework. Through a feature projection procedure and using a univariate ranking scheme as gene-selection strategy, we further designed an alternative microarray analysis model, natural kernel-imbedded Gaussian Process (NKIGP). In the end, embedded with a reversible jump Markov chain Monte Carlo (RJMCMC) model, we present an efficient algorithm with a cascading structure to unify the proposed methods of this study. The simulated examples demonstrate that, our method performs almost always close to the Bayesian bound in both the cases with linear Bayesian classifiers and the cases with very non-linear Bayesian classifiers. Even with mislabeled training samples, our method is still robust, showing its broad usability to those microarray analysis problems that linear methods may work flakily. Six published microarray datasets were analyzed in this study. The results show that predictive performance of our method for all these datasets is better than or at least as good as that of other state-of-the-art microarray analysis methods. Our method especially shows its superiority in analyzing one dataset that contains multiple suspicious mislabeled samples. For each of these datasets, we identified a set of significant genes, which can be used for further biological inspection at genome level. In summary, built on a Gaussian process model, a kernel-induced hierarchical Bayesian framework using microarray gene expression data for a cancer multi-classification problem is presented in this study. Our main contribution is a fully automated learning algorithm to solve this Bayesian model. Satisfactory results have been achieved in both the simulated examples and the real-world data studies.
Project management lessons from the British Lions rugby tour
Authors: D. C. Smith
Venue: Proceedings of the 2009 Annual Conference of the Southern African Computer Lecturers' Association (2009)

Project Management courses are often taught as capstone courses in Information Systems (IS) degrees. As IS projects become more complex there is a need for project managers to increasingly adopt leadership characteristics. This preliminary research compares management and leadership characteristics in an IS project context. It argues that the "soft skills" of leadership are essential in certain types of IS projects. Given the topical nature of the 2009 British Lions rugby tour, a case study of the 1997 Lions tour is presented, based on the book "My Pride of Lions" by Fran Cotton. This case study demonstrates successful leadership in action and identifies how that Lions tour "project" was the downfall of the mighty Springboks. The author proposes that the identified project leadership qualities should be developed by senior undergraduate students to encourage early leadership development in projects and to assist in reducing the current high incidence of project failure in the IT industry. Initial ideas on how this could be done will be presented.
Domain adaptation from multiple sources via auxiliary classifiers
Authors: Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target domain by leveraging a set of pre-computed classifiers (referred to as auxiliary/source classifiers) independently learned with the labeled patterns from multiple source domains. We introduce a new data-dependent regularizer based on smoothness assumption into Least-Squares SVM (LS-SVM), which enforces that the target classifier shares similar decision values with the auxiliary classifiers from relevant source domains on the unlabeled patterns of the target domain. In addition, we employ a sparsity regularizer to learn a sparse target classifier. Comprehensive experiments on the challenging TRECVID 2005 corpus demonstrate that DAM outperforms the existing multiple source domain adaptation methods for video concept detection in terms of effectiveness and efficiency.
iPhone UK
Authors: David Pogue
Venue:  (2007)

"iPhone UK: The Missing Manual" is a thorough, fun and distinctly UK-flavoured look at the phenomenal new wonder-gadget from Apple, the iPhone. Written by "New York Times" columnist and "Missing Manual" series creator David Pogue, this magnificent book shows you everything you need to know to get the most out of your new iPhone. Full of humour, tips, tricks, and surprises, "iPhone UK: The Missing Manual" teaches you how to extend iPhone's usefulness by exploiting its links to the Web as well as its connection to Macs or PCs; how to save money using Internet-based messages instead of phone calls; and how to fill the iPhone with TV shows and DVDs for free. It includes a whole chapter on O2, the iPhone's mobile carrier in the UK. 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
Comparing sets of positive and negative arguments: Empirical assessment of seven qualitative rules
Authors: Jean-François Bonnefon, Hélène Fargier
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)

Many decisions can be represented as bipolar, qualitative sets of arguments: Arguments can be pros or cons, and ranked according to their importance, but not numerically evaluated. The problem is then to compare these qualitative, bipolar sets. In this paper (a collaboration between a computer scientist and a psychologist), seven procedures for such a comparison are empirically evaluated, by matching their predictions to choices made by 62 human participants on a selection of 33 situations. Results favor cardinality-based procedures, and in particular one that allows for the internal cancellation of positive and negative arguments within a decision.
Combining Illumination Normalization Methods for Better Face Recognition
Authors: Bas Boom, Qian Tao, Luuk Spreeuwers, Raymond Veldhuis
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.
Benchmarking Performance in Retail Chains: An Integrated Approach
Authors: Dinesh Kumar Gauri, Janos Gabor Pauler, Minakshi Trivedi
Venue: Marketing Science (2009)

Standardizing performance expectations across different outlets within a chain, differing in their individual features, their consumers, and the nature of competition they face, can be an onerous task. We develop an integrated, nonlinear, block group-level market share model of store expectations that draws upon the existing trade area as well as store performance literatures. By incorporating and normalizing a large number of external and internal factors impacting performance, we are able to offer a means for the retailer to determine equitable standards. The model is estimated using a variation of the maximum-likelihood estimation, on a data set fashioned from several sources and aggregated at the block group and store levels. Finally, we propose a set of indices that allows us to evaluate relative performances of stores and regions given the competitive environments they face. We find that a block group-level model offers a better fit, as well as significantly richer implications, than a traditional store-level model. Results show that a significant number of stores operate well below their expected levels, an insight not obvious from the raw numbers used to report store statistics to upper management.
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Authors: Gaurav Bansal, Atish Sinha, Huimin Zhao
Venue: Journal of Management Information Systems (2008)

Real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. While cost-sensitive learning methods for classification problems have been extensively studied recently, cost-sensitive regression has not been adequately addressed in the data mining literature yet. In this paper, we first advocate the use of average misprediction cost as a measure for assessing the performance of a cost-sensitive regression model. We then propose an efficient algorithm for tuning a regression model to further reduce its average misprediction cost. In contrast with previous statistical methods, which are tailored to particular cost functions, this algorithm can deal with any convex cost functions without modifying the underlying regression methods. We have evaluated the algorithm in bank loan charge-off forecasting, where underforecasting is considered much more costly than overforecasting. Our results show that the proposed algorithm significantly reduces the average misprediction costs of models learned with various base regression methods, such as linear regression, model tree, and neural network. The amount of cost reduction increases as the difference between the unit costs of the two types of errors (overprediction and underprediction) increases.
Stream data clustering based on grid density and attraction
Authors: Li Tu, Yixin Chen
Venue: ACM Transactions on Knowledge Discovery from Data (TKDD) (2009)

Clustering real-time stream data is an important and challenging problem. Existing algorithms such as CluStream are based on the k-means algorithm. These clustering algorithms have difficulties finding clusters of arbitrary shapes and handling outliers. Further, they require the knowledge of k and user-specified time window. To address these issues, this article proposes D-Stream, a framework for clustering stream data using a density-based approach. Our algorithm uses an online component that maps each input data record into a grid and an offline component that computes the grid density and clusters the grids based on the density. The algorithm adopts a density decaying technique to capture the dynamic changes of a data stream and a attraction-based mechanism to accurately generate cluster boundaries. Exploiting the intricate relationships among the decay factor, attraction, data density, and cluster structure, our algorithm can efficiently and effectively generate and adjust the clusters in real time. Further, a theoretically sound technique is developed to detect and remove sporadic grids mapped by outliers in order to dramatically improve the space and time efficiency of the system. The technique makes high-speed data stream clustering feasible without degrading the clustering quality. The experimental results show that our algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data streams.
A method for land consolidation progress assessment based on GPS and PDA
Authors: Guangming Zhu, Yingyi Chen, Daoliang Li
Venue: WSEAS Transactions on Computers (2009)

Field survey is a traditional method in the process of land consolidation. But it has such as low efficiency, long time, and some other shortcomings in measuring length, area and calculating the amount of small object, especially for the project area with more complex terrain. The paper puts forward using GPS/PDA to calculate progress for project of land consolidation, which propels the application of GPS/PDA in the fields of land consolidation. The paper introduces the principle of GPS, interface of GPS and PDA, realize real time interaction between the field of project area and the electronic maps by using GPS's location function, on the basis of which accomplishes real time judgment for project land features' progress, quantities and some other accuracy of information.