ppxscal/embedding_network huggingface dataset:
MARS: a system for publishing XML from mixed and redundant storage
Authors: Alin Deutsch, Val Tannen
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

We present a system for publishing as XML data from mixed (relational+XML) proprietary storage, while supporting redundancy in storage for tuning purposes. The correspondence between public and proprietary schemas is given by a combination of LAV-and GAV-style views expressed in XQuery. XML and relational integrity constraints are also taken into consideration. Starting with client XQueries formulated against the public schema the system achieves the combined effect of rewriting-with-views, composition-with-views and query minimization under integrity constraints to obtain optimal reformulations against the proprietary schema. The paper focuses on the engineering and the experimental evaluation of the MARS system.
Review:
Authors: Alice J. O'Toole, Fang Jiang, Hervé Abdi, Nils Pénard, Joseph P. Dunlop, Marc A. Parent
Venue: Journal of Cognitive Neuroscience (2007)

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These “brain-reading” analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.
Learning English grammar with a corpus: Experimenting with concordancing in a university grammar course
Authors: Maria Estling Vannestål, Hans Lindquist
Venue: ReCALL (2007)

Corpora have been used for pedagogical purposes for more than two decades but empirical studies are relatively rare, particularly in the context of grammar teaching. The present study focuses on students' attitudes towards grammar and how these attitudes are affected by the introduction of concordancing. The principal aims of the project were to increase the students' motivation by showing them that English grammar is more than a set of rules in a book and to enable them to assume more responsibility for their own learning. The idea was to introduce the use of language corpora into the curriculum for first-semester English at Växjö University in Sweden, as a complement to grammar textbooks and ordinary exercise materials. Between classes, the students worked with problem-solving assignments that involved formulating their own grammar rules based on the examples they found in the corpus. In the classroom, a system of peer teaching was applied, where the students took turns at explaining grammatical rules to each other. Besides presenting a new way of working with grammar, we also provided the students with a tool for checking questions of usage when writing English texts in the future, since the corpus we use is free of charge and available to all. The work with corpora and peer teaching was evaluated by means of questionnaires and interviews. This article describes and evaluates this initiative and presents insights gained in the process. One important conclusion is that using corpora with students requires a large amount of introduction and support. It takes time and practice to get students to become independent corpus users, knowing how to formulate relevant corpus queries and interpret the results. Working with corpora is a method that some students appreciate while others, especially weak students, find it difficult or boring. Several of the students did not find corpora very useful for learning about grammatical rules, but realized the potential of using corpora when writing texts in English.
Generative Models for Fingerprint Individuality using Ridge Types
Authors: Gang Fang, Sargur N. Srihari, Harish Srinivasan
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

Generative models of pattern individuality attempt to represent the distribution of observed quantitative features, e.g., by learning parameters from a database, and then use such distributions to determine the probability of two random patterns being the same. Considering fingerprint patterns, Gaussian distributions have been previously used for minutiae location and von-Mises distributions for minutiae orientation so as to determine the probability of random correspondence (PRC) between two fingerprints. Motivated by the fact that ridges have not been modeled in generative models, and using representative ridge points in fingerprint matching, ridge information is incorporated into the generative model by using a third distribution for ridge types. The joint probability of minutiae location, minutiae orientation and ridge type is modeled as a mixture distribution. The proposed model offers a more accurate fingerprint representation from which more reliable PRCs can be computed. Based on parameters estimated from fingerprint databases, PRCs using ridge types are seen to be much smaller than PRCs computed with only minutiae.
Properties of nonlinear confidence estimates for statistically uncertain systems
Authors: N. V. Medvedeva, G. A. Timofeeva
Venue: Automation and Remote Control (2007)

Consideration is given to the problem of confidence estimation for statistically uncertain systems with observation, i.e., for systems in the description of which there are both random perturbations with known distributions and unknown perturbations with the information only about domains of their possible values. Confidence estimates for a state of the system are designed according to the earlier proposed method based on the use of random information sets. The behavior of confidence sets when variances of nonrandom perturbations tend to zero and sets of uncertainty of random perturbations contract to a point is studied.
A Unified-Index Based Distributed Specification for Heterogeneous Components Management
Authors: Ming Zhong, Yaoxue Zhang, Pengwei Tian, Cunhao Fang, Yuezhi Zhou
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

With the development of service-oriented architecture (SOA) and component-based software engineering (CBSE), the number of various heterogeneous service components increases rapidly. They are stored in traditional repositories of different organization and can be independently accessed by users. However, it brings difficulty in component discovery and reuse. The utility of massive component resource can not be remarkably improved. In this paper, a Unified-Index (UI) based distributed specification for heterogeneous components management is introduced. It focuses on creating and maintaining a Unified-Index tree on the UI repository server, which has a management field grouped by some traditional repositories. Each UI repository server maps the components of its children traditional repositories into UI format and stores them in local UI repository. It also actively monitors the adding of new components and removing of invalid ones. Every query of component discovery will be diffused to all of the UI repository servers with transmitting of the root server. So in this desired architecture, users can consistently search and access all the heterogeneous components for further assembly and application, satisfying various personalized and comprehensive user requirements.
Boosting the Performance of Web Spam Detection with Ensemble Under-Sampling Classification
Authors: Guang-Gang Geng, Chun-Heng Wang, Qiu-Dan Li, Lei Xu, Xiao-Bo Jin
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Anti-spam has become one of the top challenges for the Web search. In this paper, we explore the web spam de- tection as a binary classification problem. Based on the fact that reputable pages are more easy to be obtained than spam ones on the Web, an ensemble under-sampling classi- fication strategy is adopted, which exploits the information involved in the large number of reputable websites to full advantage. The strategy is based on the predicted spamic- ity of every sub-classifiers, in which both content-based and link-based features are taken into account. The experiments on standard WEBSPAM-UK2006 benchmark showed that the ensemble strategy can improve the web spam detection performance effectively.
A metrics framework for evaluating group formation
Authors: Asma Ounnas, David E. Millard, Hugh C. Davis
Venue: Proceedings of the 2007 international ACM conference on Supporting group work (2007)

Many approaches to learning and teaching rely upon students working in groups. So far, many Computer-Supported Group Formation systems have been designed to facilitate the formation of optimal groups in learning. However, evaluating the quality of automated group formation is not always well reported. In this paper we propose a metrics framework for evaluating group formation based upon a model for constraint satisfaction-based group formation.
On the Parallel Evaluation of Polynomials
Authors: K. Maruyama
Venue: IEEE Transactions on Computers (1973)

If an unlimited number of processors is available, then for any given number of steps s, s=1, polynomials of degree as large as C2n-dcan be evaluated, where C= v2 and d  v2s. This implies polynomials of degree can be evaluated in log2n+v2log2n +0(1) steps. Various techniques for the evaluation of polynomials in a "reasonable number" of "steps" are compared with th
Drums, curve descriptors and affine invariant region matching
Authors: M. Zuliani, L. Bertelli, C. S. Kenney, S. Chandrasekaran, B. S. Manjunath
Venue: Image and Vision Computing (2008)

In this paper we present a new physically motivated curve/region descriptor based on the solution of Helmholtz's equation. The descriptor we propose satisfies the six principles set by MPEG-7: it has a good retrieval accuracy, it is compact, it can be applied in general contests, it has a reasonable computational complexity, it is rotation and scale invariant and provides an hierarchical representation of the curve from coarse to fine. In addition to these properties, the descriptor can be generalized in order to take into account also the intensity content of the image region defined by the curve. The construction of the descriptor can be coupled with a preprocessing step that enables us to describe a curve in an affine invariant fashion. The performance of our approach has been tested in the contest of affine invariant curve and region matching, both within a controlled experimental setup and also using real images. The experiments show that the proposed approach compares favorably to the state of the art curve/region descriptors.
Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks
Authors: Dan Wu, Libing Wu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Traditionally, a single junction tree is used as the secondary structure for inference in a Bayesian network. However, its applicability and efficiency are restricted by the size of the junction tree. In this paper, we demonstrate that using a hierarchy of junction trees (HJT) as the secondary structure instead will greatly alleviate this restriction and improve the performance. We also compare the proposed HJT with other similar schemes for inference in Bayesian networks.
Allocation strategies for utilization of space-shared resources in Bag of Tasks grids
Authors: César A. F. De Rose, Tiago Ferreto, Rodrigo N. Calheiros, Walfredo Cirne, Lauro B. Costa, Daniel Fireman
Venue: Future Generation Computer Systems (2008)

As the adoption of grid computing in organizations expands, the need for wise utilization of different types of resource also increases. A volatile resource, such as a desktop computer, is a common type of resource found in grids. However, using efficiently other types of resource, such as space-shared resources, represented by parallel supercomputers and clusters of workstations, is extremely important, since they can provide a great amount of computation power. Using space-shared resources in grids is not straightforward since they require jobs a priori to specify some parameters, such as allocation time and amount of processors. Current solutions (e.g. Grid Resource and Allocation Management (GRAM)) are based on the explicit definition of these parameters by the user. On the other hand, good progress has been made in supporting Bag-of-Tasks (BoT) applications on grids. This is a restricted model of parallelism on which tasks do not communicate among themselves, making recovering from failures a simple matter of reexecuting tasks. As such, there is no need to specify a maximum number of resources, or a period of time that resources must be executing the application, such as required by space-shared resources. Besides, this state of affairs makes leverage from space-shared resources hard for BoT applications running on grid. This paper presents an Explicit Allocation Strategy, in which an adaptor automatically fits grid requests to the resource in order to decrease the turn-around time of the application. We compare it with another strategy described in our previous work, called Transparent Allocation Strategy, in which idle nodes of the space-shared resource are donated to the grid. As we shall see, both strategies provide good results. Moreover, they are complementary in the sense that they fulfill different usage roles. The Transparent Allocation Strategy enables a resource owner to raise its utilization by offering cycles that would otherwise go wasted, while protecting the local workload from increased contention. The Explicit Allocation Strategy, conversely, allows a user to benefit from the accesses she has to space-shared resources in the grid, enabling her natively to submit tasks without having to craft (time, processors) requests.
Fast Nearest Neighbor Condensation for Large Data Sets Classification
Authors: Fabrizio Angiulli
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

This work has two main objectives, namely, to introduce a novel algorithm, called the Fast Condensed Nearest Neighbor (FCNN) rule, for computing a training set consistent subset for the nearest neighbor decision rule, and to show that condensation algorithms for the nearest neighbor rule can be applied to huge collections of data. The FCNN rule has some interesting properties: it is order independent, its worst case time complexity is quadratic but often with a small constant pre-factor, and it is likely to select points very close to the decision boundary. Furthermore, its structure allows for the triangular inequality to be effectively exploited to reduce the computational effort. The FCNN rule outperformed even here enhanced variants of existing competence preservation methods both in terms of learning speed and learning scaling behavior, and often in terms of the size of the model, while it guaranteed the same prediction accuracy. Furthermore, it was three order of magnitude faster than hybrid instance-based learning algorithms on the MNIST and MIT Face databases and computed a model of accuracy comparable to that of methods incorporating a noise filtering pass.
Global and Modular Two's Complement Cellular Array Multipliers
Authors: Kai Hwang
Venue: IEEE Transactions on Computers (1979)

Two new families of LSI iterative logic arrays are proposed to perform two's complement multiplication based on the Baugh-Wooley algorithm [2]. The global approach is faster and attractive for LSI but limited in size due to current monolithic and packaging technology. The modular approach is better suited to realizing arbitrarily large array multipliers at only slight decrease in speed. The proposed additive multiply modules can be externally programmed by hardwiring to multiply binary numbers in either two's complement or unsigned format. No peripheral logic circuits such as Wallace trees or complementers are needed in constructing the proposed modular multiplication networks. Speed analysis, hardware complexity, packaging, and application requirements of the proposed array multipliers are also provided.
A Feasibility Study on Defending Against Ultra-Fast TopologicalWorms
Authors: Liang Xie, Sencun Zhu
Venue: Proceedings of the Seventh IEEE International Conference on Peer-to-Peer Computing (2007)

Self-propagating worms have been terrorizing the Internet for several years and they are becoming imminent threats to large-scale Peer-to-Peer (P2P) systems featuring rich host connectivity and popular data services. In this paper, we consider topological worms, which exploit P2P host vulnerabilities and topology information to spread in an ultra-fast way. We study the feasibility of leveraging the existing P2P overlay structure for distributing automated security patches to vulnerable machines. Two approaches are examined: a partition-based approach, which utilizes immunized hosts to proactively stop worm spread in the overlay graph, and a Connected Dominating Set(CDS)-based approach, which utilizes a group of dominating nodes in the overlay to achieve fast patch dissemination in a race with the worm. We demonstrate through analysis and simulations that both methods can result in effective worm containment.
An Effective Maximal Subspace Clustering Algorithm Based on Enumeration Tree
Authors: Jian Yin, Zhilan Huang, Yubao Liu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

Subspace clustering is one of the best approaches for discovering meaningful clusters in high dimensional space. However, the existing algorithms often produce clusters of great redundancy that are not easy to be understood. In this paper, based on the enumeration tree of subspace, we propose a new subspace clustering algorithm MSC to find the clusters hidden in the maximal subspace. MSC uses the monotony of cluster distribution in subspace to prune the enumeration tree of subspace, and uses the simple set intersection operation of subspace to generate the clusters. Compared to the existing subspace clustering algorithm, the experimental results confirm MSC is effective for the subspace clustering.
Knowledge Management in Virtual Communities of Practice: Experiments with a Helper Tool
Authors: Junio Cesar de Lima, Cedric Luiz de Carvalho
Venue: Proceedings of the International Conference on Semantic Computing (2007)

The web was projected to support resource sharing at global level. Nowadays, this resource sharing is very limited, mainly respect to imperfect information retrieval and hidden web. The semantic web comes up as a possible solution to such limitations. The virtual communities of practice are groups of people who get in with each other virtually and share their knowledge. The use of semantic web technologies can be very useful in construction of tools to support these communities. This text presents a tool constructed to help knowledge management in virtual communities of practice. It was developed based on the semantic web principles.
An Automatic DFT System for the Silc Silicon Compiler
Authors: H. s. Fung, S. Hirschhorn
Venue: IEEE Design Test (1986)

This article discusses design for testability automation for the Silc silicon compiler under development at GTE Laboratories,Inc. Our modular design for testability uses both built-in self-test and scan-path techniques for Slic's full custom VLSIdesigns. A test controller coordinates the testing of the chip's modules. Testability evaluation is performed using controllability/observabilitymethods, and using a method based on information theory. A testable-by-construction approach is followed in order to synthesizeblocks of testable logic. A testability ?expert? manages testability knowledge during the synthesis process and makes theultimate testability decisions.
A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances
Authors: Camilo P. Tenorio, Francisco de A. T. de Carvalho, Julio T. Pimentel
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a fuzzy clustering algorithm to partitioning symbolic interval data. The proposed method furnish a fuzzy partition and a prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare symbolic interval data, the method use a suitable adaptive Mahalanobis disance defined on vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.
Adaptive Fuzzy Immune Sliding Mode Control for a Class of Uncertain Nonlinear Systems
Authors: Xiangyang Yu, Feng Yang, Yongxuan Huang, Haipeng Nan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

In this paper, a stable adaptive fuzzy immune sliding mode controller(AFISMC) for a class of nonlinear uncertain systems is investigated. The fuzzy system is applied to approximate the unknown nonlinear function of plant, and then the estimation value of nonlinear function is used to design the control variable, the immune feedback system is used as a compensator for the fuzzy sliding control. The proposed approach does not need a bound to be known, only requiring that it exists, and has not any assumption for initial condition. The approach is applied to an inverted pendulum system via significant disturbances. Analysis of simulations reveals that proposed method is robust in the presence of uncertainties and bounded external disturbances.
Personal Computers Go to Work
Authors: H. Garland
Venue: Computer (1978)

Several small-scale computer applications illustrate the usefulness of microprocessor-based systems for a variety of industrial processes and business procedures.