ppxscal/embedding_network huggingface dataset:
The Bayesian group-Lasso for analyzing contingency tables
Authors: Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.
Gene Specific Co-regulation Discovery: An Improved Approach
Authors: Ji Zhang, Qing Liu, Kai Xu
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene, identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved efficiency. We employ kNN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the current method. 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
Towards collaborative data reduction in stream-processing systems
Authors: Ming Li, David Kotz
Venue: International Journal of Communication Networks and Distributed Systems (2009)

We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a collaborative data-reduction mechanism, 'group-aware stream filtering', used together with multicast, to select a small set of necessary data that satisfy the needs of a group of subscribers simultaneously. We turn data-compressing filters into group-aware filters by exploiting two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of 'slack' in their data quality requirements; 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the 'best alternative' subset for each application to maximise the data overlap within the group to best benefit from multicasting. We provide a general framework that treats the group-aware stream filtering problem completely; we prove the problem NP-hard and thus provide a suite of heuristic algorithms that ensure data quality (specifically, granularity and timeliness) while collaboratively reducing data. The framework is extensible and supports a diverse range of filters. Our prototype-based evaluation shows that group-aware stream filtering is effective in trading CPU time for data reduction, compared with self-interested filtering.
Econometric models applied in study of unemployment rate evolution in Romania
Authors: Rodica Manuela Gogonea, Marian Zaharia, Stefan Alexandru Ionescu
Venue: Proceedings of the 10th WSEAS international conference on Fuzzy systems (2009)

Unemployment can be considered a global macrosocial flow generated by causes that have to be the economic situation of users, or the status of tenders for work. On the one hand the emergence of unemployment, on the other hand, its maintenance and alteration represent aspects that may be considered only in connection with the real system of market economy. A statistico-econometric analysis of unemployment can be achieved either by setting the unemployment evolution, or on the other plan, through a study of correlation that can be establish between this measurement indicator of unemployment and the main factors of influence. An analysis of the evolution and the structure of unemployment will be realized on two subperiods: 1992-1999 and 2000-2006, because, the conditions of the rate of unemployment formation are different, taking into account that Romania was reorganized and placed in economically with the millennium XX changeover.
Surface passivation and implications on high mobility channel performance (Invited Paper)
Authors: C. L. Hinkle, M. Milojevic, E. M. Vogel, R. M. Wallace
Venue: Microelectronic Engineering (2009)

We review our recent studies of the passivation of the GaAs and InGaAs surface using a combination of insitu and ex situ surface analysis and capacitor measurements. We find that the control of Ga-oxides in particular appears to play an important role in understanding the characteristics of III-V MOS devices.
An association analysis approach to biclustering
Authors: Gaurav Pandey, Gowtham Atluri, Michael Steinbach, Chad L. Myers, Vipin Kumar
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

The discovery of biclusters, which denote groups of items that show coherent values across a subset of all the transactions in a data set, is an important type of analysis performed on real-valued data sets in various domains, such as biology. Several algorithms have been proposed to find different types of biclusters in such data sets. However, these algorithms are unable to search the space of all possible biclusters exhaustively. Pattern mining algorithms in association analysis also essentially produce biclusters as their result, since the patterns consist of items that are supported by a subset of all the transactions. However, a major limitation of the numerous techniques developed in association analysis is that they are only able to analyze data sets with binary and/or categorical variables, and their application to real-valued data sets often involves some lossy transformation such as discretization or binarization of the attributes. In this paper, we propose a novel association analysis framework for exhaustively and efficiently mining "range support" patterns from such a data set. On one hand, this framework reduces the loss of information incurred by the binarization- and discretization-based approaches, and on the other, it enables the exhaustive discovery of coherent biclusters. We compared the performance of our framework with two standard biclustering algorithms through the evaluation of the similarity of the cellular functions of the genes constituting the patterns/biclusters derived by these algorithms from microarray data. These experiments show that the real-valued patterns discovered by our framework are better enriched by small biologically interesting functional classes. Also, through specific examples, we demonstrate the ability of the RAP framework to discover functionally enriched patterns that are not found by the commonly used biclustering algorithm ISA. The source code and data sets used in this paper, as well as the supplementary material, are available at http://www.cs.umn.edu/vk/gaurav/rap.
Coalition Structures in Weighted Voting Games
Authors: Edith Elkind, Georgios Chalkiadakis, Nicholas R. Jennings
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Weighted voting games are a popular model of collaboration in multiagent systems. In such games, each agent has a weight (intuitively corresponding to resources he can contribute), and a coalition of agents wins if its total weight meets or exceeds a given threshold. Even though coalitional stability in such games is important, existing research has nonetheless only considered the stability of the grand coalition. In this paper, we introduce a model for weighted voting games with coalition structures. This is a natural extension in the context of multiagent systems, as several groups of agents may be simultaneously at work, each serving a different task. We then proceed to study stability in this context. First, we define the CS-core, a notion of the core for such settings, discuss its non-emptiness, and relate it to the traditional notion of the core in weighted voting games. We then investigate its computational properties. We show that, in contrast with the traditional setting, it is computationally hard to decide whether a game has a non-empty CS-core, or whether a given outcome is in the CS-core. However, we then provide an efficient algorithm that verifies whether an outcome is in the CS-core if all weights are small (polynomially bounded). Finally, we also suggest heuristic algorithms for checking the non-emptiness of the CS-core.
Selling ad campaigns: online algorithms with cancellations
Authors: Moshe Babaioff, Jason D. Hartline, Robert D. Kleinberg
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

We study online pricing problems in markets with cancellations, i.e., markets in which prior allocation decisions can be revoked, but at a cost. In our model, a seller receives requests online and chooses which requests to accept, subject to constraints on the subsets of requests which may be accepted simultaneously. A request, once accepted, can be canceled at a cost which is a fixed fraction of the request value. This scenario models a market for web advertising campaigns, in which the buyback cost represents the cost of canceling an existing contract. We analyze a simple constant-competitive algorithm for a single-item auction in this model, and we prove that its competitive ratio is optimal among deterministic algorithms, but that the competitive ratio can be improved using a randomized algorithm. We then model ad campaigns using knapsack domains, in which the requests differ in size as well as in value. We give a deterministic online algorithm that achieves a bi-criterion approximation in which both approximation factors approach 1 as the buyback factor and the size of the maximum request approach 0. We show that the bi-criterion approximation is unavoidable for deterministic algorithms, but that a randomized algorithm is capable of achieving a constant competitive ratio. Finally, we discuss an extension of our randomized algorithm to matroid domains (in which the sets of simultaneously satisfiable requests constitute the independent sets of a matroid) as well as present results for domains in which the buyback factor of different requests varies.
XQuery as a retrieval mechanism for longitudinal multiscale forest resource data
Authors: Jussi Rasinmäki
Venue: Environmental Modelling Software (2009)

In most forested environments, boreal forest in particular, monitoring and evaluation of management activities would be facilitated by storage of multiscale data sets spanning long time periods. For this study, such a data repository based on a generic spatio-temporal conceptual data model was created using XML. The data repository containing changes in data content for a 1000ha forest estate between 1871 and 1994 was used to evaluate the data retrieval capabilities of XQuery. The data retrieval operations consisted of pivoting, sequencing, and aggregation. Pivoting transforms the generic attribute storage used in the data model into a more compact column-wise format. Sequencing is used to return a temporal sequence of values, and for the traversal of the object hierarchy. Aggregation aggregates the values of a variable over a grouping variable. The combinations of the basic retrieval operations were also investigated as well as the effect of time and object hierarchy on the retrieval operations. The conceptual match between the generic spatio-temporal data model and the XML data model was apparent at the data retrieval operations. The key properties of XQuery for the implementation of the retrieval operations were extendability, support for nested structures, and data transformation functionality.
Data-driven Induction of Functional Programs
Authors: Emanuel Kitzelmann
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We present a new method and system, called IGOR2, for the induction of recursive functional programs from few nonrecursive, possibly non-ground example equations describing a subset of the input-output behaviour of a function to be implemented.
Online slant signature algorithm analysis
Authors: Azlinah Mohamed, Rohayu Yusof, Sofianita Mutalib, Shuzlina Abdul Rahman
Venue: WSEAS Transactions on Computers (2009)

A vector rule-based approach and analysis to on-line slant signature recognition algorithm is presented. Extracting features in signature is an intense area due to complex human behavior, which is developed through repetition. Features such as direction, slant, baseline, pressure, speed and numbers of pen ups and downs are some of the dynamic information signature that can be extracted from an online method. This paper presents the variables involve in designing the algorithm for extracting the slant feature. Signature Extraction Features System (SEFS) is used to extract the slant features in signature automatically for analysis purposes. The system uses both local and global slant characteristics in extracting the feature. Local slant is the longest slant among the detected slant while the global slant represents the highest quantity of classified slant whether the slant are leftward, upright or rightward. Development and analysis are reported on a database comprises of 20 signatures from 20 subjects. The system is compared to human expert evaluation. The results demonstrate a competitive performance with 85% accuracy.
An extended JADE-S based framework for developing secure Multi-Agent Systems
Authors: Salvatore Vitabile, Vincenzo Conti, Carmelo Militello, Filippo Sorbello
Venue: Computer Standards Interfaces (2009)

Agent communities are self-organized virtual spaces consisting of a large number of agents and their dynamic environments. Within a community, agents group together offering special e-services for effective, reliable, and mutual benefits. Usually, an agent community is composed of specialized agents performing one or more tasks in a single domain/sub-domain, or in highly intersecting domains. However, secure Multi-Agent Systems require severe mechanisms in order to prevent malicious attacks. Several limits affect exiting secure agents platform, such as the lack of a strong authentication system, the lack of a flexible distributed mechanism for access control and the lack of a system for storing past behaviors of agent/user. Biometric owner agents authentication, agent/users policies to regulate agent's behavior and actions, and agent/users reputation level to select trusted agents can be used to overcome the above limits and enhance the level of security for these applications. In this paper an extended JADE-S based framework for developing secure Multi-Agent Systems is proposed. The framework functionalities are extended by self-contained FPGA biometric sensors providing secure and fast user authentication service. Each agent owner, by means of biometric authentication, acquires his/her own X.509v3 digital certificate. Policy files and a flexible, fast distributed Access Control Mechanism can regulate behavior and actions of any users/agent inside the platform. In addition, a mechanism based on the agent reputation is used: reputation is an attribute associated to each owner and/or agent on the basis of its past behavior and integrity. In order to prove the feasibility of the proposed framework, we have developed a multi-agent e-Banking system. System goal deals with e-Banking services such as bank account statements, account transactions and so on. In the paper, the experimental features of the biometric self-contained sensors are also outlined.
Proactive database index tuning through data threshold prediction
Authors: Jeff Magnusson, Douglas D. Dankel,  II
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

For queries to be efficiently processed in a database, the query optimizer must be presented with an efficient set of indexes for the query workload. Due to the dynamic and complex nature of modern database workloads, database administrators and developers often rely on autonomic tuning tools to determine an optimal set of indexes for the workload. These autonomic tuning tools rely on the use of virtual, or "what-if", indexes. Virtual indexes are limited in the respect that they only provide tuning recommendations given the sizes and distribution of data currently housed in the database. In an active database system, relations and the distribution of data within them are constantly changing at different relative rates. The optimal set of indexes for completing a query will change when related data distributions grow relative to one another past some data threshold. In this paper, we propose a novel extension to the virtual index concept that allows estimating query behavior and index utilization at future points in time. Thus, data thresholds can be proactively detected and the future viability of a given index set can be assessed. The implementation and various applications of this new technology are discussed.
Automatic replication of WSRF-based Grid services via operation providers
Authors: G. Moltó, V. Hernández, J. M. Alonso
Venue: Future Generation Computer Systems (2009)

The advent of service-oriented architectures in Grid environments has fostered the development of applications in distributed deployments. The Globus Toolkit 4 (GT4) and its implementation of stateful Web services, via the WS-Resource Framework (WSRF), is a suitable platform to develop these Grid services. This way, its increased usage in many scientific areas reveals new scenarios where fault-tolerance and high availability should be considered. This paper describes a library that manages the automatic replication of WSRF-based Grid services. This functionality can be plugged to existing Grid services, by means of minimal changes in its source code, to achieve state replication through WS-Resources. The architecture of the library and its performance evaluation are described. In particular, two different replica topologies are addressed: ring-based and leaf-to-root complete binary tree, in order to achieve resource state update in logarithmic time with respect to the number of replicas. Finally, the paper describes the integration of the replication library into a service-oriented metascheduler to enhance fault-tolerance and to guarantee service availability.
Against Idiosyncrasy in Ontology Development
Authors: Barry Smith
Venue: Proceeding of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006) (2006)

The world of ontology development is full of mysteries. Recently, ISO Standard 15926 (“Lifecycle Integration of Process Plant Data Including Oil and Gas Production Facilities”), a data model initially designed to support the integration and handover of large engineering artefacts, has been proposed by its principal custodian for general use as an upper level ontology. As we shall discover, ISO 15926 is, when examined in light of this proposal, marked by a series of quite astonishing defects, which may however provide general lessons for the developers of ontologies in the future.
Drama prototyping for the design of urban interactive systems for children
Authors: Andrea Alessandrini, Antonio Rizzo, Elisa Rubegni
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

In this paper, we present Wi-swing, a case study approach in designing a networked playground for children located in a public garden. The approach adopted in the case study is based on prototyping using the technique of scenario dramatization. The process of designing augmented interactive systems needs to be addressed to consider the physical, aesthetic and social aspects of interaction. In the case study illustrated in this paper, the design process is based on prototyping using the scenario dramatization technique. This technique allows us to explore and investigate the possible features of interactive artifacts focusing on the potential of the technology and the emergent practices. Wi-swing is the concept developed using dramatization: a tool for listening to stories in public spaces, controlled entirely by children. In the paper we show the results of the early design process and the development of the concepts by scenario dramatization. In what follows, we describe a case study in which we applied this technique for the development of an interactive urban playground through which children can be authors of the stories to which they are listening.
The properties of differential-algebraic equations representing optimal control problems
Authors: Roland England, Susana Gómez, René Lamour
Venue: Applied Numerical Mathematics (2009)

A procedure is described for transforming a general optimal control problem to a system of Differential-Algebraic Equations (DAEs). The Kuhn-Tucker conditions consist of differential equations, complementarity conditions and corresponding inequalities. The latter are converted to equalities by adding a new variable combining the slack variable and the corresponding Lagrange multiplier. We investigate the properties of the resulting DAEs. The index of a system of DAEs determines the well-conditioning of the problem. The concept of the tractability index is used to investigate the index in a systematic way, and during this process, it indicates which components of the system of equations must be differentiated to reduce the index. For an index-3 problem, the index is reduced without increasing the number of equations, and a numerical procedure is used to determine the index. In the examples used here, the DAEs can be solved analytically. The examples are tested by the numerical determination of the index, and the results confirm the previously known properties of these examples. The reformulation proposed here, as well as the index determination, might be used in the future, to develop a methodology to solve optimal control problems.
Fuzzy C-Means Cluster Segmentation Algorithm Based on Modified Membership
Authors: Yanling Li, Gang Li
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is noise sensitive because of not taking into account the spatial information in the image. In this paper, we present fuzzy c-means cluster segmentation algorithm based on modified membership (MFCMp,q) that incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. The fast MFCMp,q algorithm(FMFCMp,q) which speeds up the convergence of MFCMp,q algorithm is achieved when the MFCMp,q algorithm is initialized by the fast fuzzy c-means algorithm based on statistical histogram. The experiments on the artificial synthetic image and real-world datasets show that MFCMp,q algorithm and FMFCMp,q algorithm can segment images more effectively and provide more robust segmentation results.