ppxscal/embedding_network huggingface dataset:
MetaFac: community discovery via relational hypergraph factorization
Authors: Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi Konuru, Hari Sundaram, Aisling Kelliher
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.
Front Matter and Contents
Venue: Proceeding of the 2009 conference on A Class of Algorithms for Distributed Constraint Optimization (2009)

Multi Agent Systems (MAS) have recently attracted a lot of interest because of their ability to model many real life scenarios where information and control are distributed among a set of different agents. Practical applications include planning, scheduling, distributed control, resource allocation, etc. A major challenge in such systems is coordinating agent decisions, such that a globally optimal outcome is achieved. Distributed Constraint Optimization Problems (DCOP) are a framework that recently emerged as one of the most successful approaches to coordination in MAS. This thesis addresses three major issues that arise in DCOP: efficient optimization algorithms, dynamic and open environments, and manipulations from self-interested users. We make significant contributions in all these directions: Efficiency-wise, we introduce a series of DCOP algorithms, which are based on dynamic programming, and largely outperform previous DCOP algorithms. The basis of this class of algorithms is DPOP, a distributed algorithm that requires only a linear number of messages, thus incurring low networking overhead. For dynamic environments we introduce self-stabilizing algorithms that can deal with changes and continuously update their solutions. For self interested users, we propose the M-DPOP algorithm, which is the first DCOP algorithm that makes honest behaviour an ex-post Nash equilibrium by implementing the VCG mechanism distributedly. We also discuss the issue of budget balance, and introduce two algorithms that allow for redistributing (some of) the VCG payments back to the agents, thus avoiding the welfare loss caused by wasting the VCG taxes.
Integrated evaluator extracted from infringement lawsuits using extension neural network accommodated to patent assessment
Authors: Yi-Hsuan Lai, Hui-Chung Che
Venue: International Journal of Computer Applications in Technology (2009)

This study aims at the basis of the patent law and proposes an integrated evaluator for patent assessment. The damage award of a patent-infringement lawsuit is deemed to be the legal value of the patent. Sixty five effective samples are extracted from 4289 patent-related lawsuits retrieved from the US district courts of Delaware, California and Texas. Seventeen indicators are summarised to describe quantitatively the dimensions of the patents. Nine extracted factors are generated from the 17 patent indicators by Factor Analysis. Extension neural network is applied to build the patent valuation model, wherein the extracted factors are the input variables and the damage award is the output variable. The patent valuation model is validated to be made feasible by error analysis. The integrated evaluator for patent is then established by transforming the output of the patent valuation model via the Z-score. The proposed integrated evaluator accommodated to the patent assessment to improve any existing financial approaches. It also contributed to the patent transaction, patent licensing, hypothecation of intangible assets, shareholding by patent-based technologies and venture capital, etc.
Plane strain sliding contact of multilayer magnetic storage thin-films using the finite element method
Authors: Raja R. Katta, Emerson Escobar Nunez, Andreas A. Polycarpou, Sung-Chang Lee
Venue: Microsystem Technologies (2009)

The sliding contact or scratch behavior of multi-layer thin-films such as those found in magnetic storage disks has been studied using the finite element method. A rigid cylinder sliding over a multilayered thin-film half-space was implemented to simulate the contact between a feature of the recording slider (such as the protrusion on the trailing edge of the slider, which is part of the thermal flying-height control, TFC) and the magnetic storage multilayer disk. The effects of different parameters such as normal load, friction coefficient and TFC radius on the von Mises, shear and principal stresses in the multilayer system were analyzed. Results showed that under sliding conditions, for a given normal load, the friction coefficient influences the location and magnitude of the plastic strain in the multilayer system. Repeated sliding contact was also performed to characterize its effect on the stress and strain behavior under various loading conditions and investigate shakedown behavior.
Family_Mediator --Adding Notions of Fairness to Those of Interests
Authors: John Zeleznikow, Emilia Bellucci
Venue: Proceeding of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference (2006)

Most negotiation support systems have focused upon the notion of meeting the disputants' interests. This neglects the vital issues of justice and power. In this article we address the issue of how to add notions of fairness to interests, through the development of the Family_Mediator system. Family_Mediator is an extension of the Family_Winner system, which advises mediators about potential trade-offs and compensation strategies for divorcing couples.
Overview of the Multiple Biometrics Grand Challenge
Authors: P. Jonathon Phillips, Patrick J. Flynn, J. Ross Beveridge, W. Todd Scruggs, Alice J. O'Toole, David Bolme, Kevin W. Bowyer, Bruce A. Draper, Geof H. Givens, Yui Man Lui, Hassan Sahibzada, Joseph A. Scallan,  Iii, Samuel Weimer
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

The goal of the Multiple Biometrics Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from biometric samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the acquisition constraints in different directions. In the Portal Challenge Problem, the goal is to recognize people from near-infrared (NIR) and high definition (HD) video as they walk through a portal. Iris recognition can be performed from the NIR video and face recognition from the HD video. The availability of NIR and HD modalities allows for the development of fusion algorithms. The Still Face Challenge Problem has two primary goals. The first is to improve recognition performance from frontal and off angle still face images taken under uncontrolled indoor and outdoor lighting. The second is to improve recognition performance on still frontal face images that have been resized and compressed, as is required for electronic passports. In the Video Challenge Problem, the goal is to recognize people from video in unconstrained environments. The video is unconstrained in pose, illumination, and camera angle. All three challenge problems include a large data set, experiment descriptions, ground truth, and scoring code.
Effect of Story Programming Embedded Problem Solving Strategies on the Problem Solving Ability
Authors: JungHo Park, JungBum Song, Sunghwan Jo, TaeWuk Lee
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

In this paper, I develop the SPM (Story Programming Model) embedded problem solving strategies and hope to examine the effect on problem solving ability and programming skill by comparing it with conventional programming education in elementary level.
Exact free vibration of webs moving axially at high speed
Authors: S. Hatami, M. Azhari, M. M. Saadatpour, P. Memarzadeh
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

Moving webs can be found in a wide range of industrial applications such as paper handling, textile manufacturing, and magnetic tape recording. The moving web in these applications, which is generally orthotropic, may experience speeds more than critical speed. Critical speed is defined as that axial speed where the system vibration has a vanishing eigenvalue and is subject to a buckling instability. At a supercritical speed, the web may experience types of instabilities and subsequently sever out of plane vibrations. In this paper, based on thin plate theory, the equation of out-of-plane motion is derived for an orthotropic web. Then an exact method is employed to evaluate free vibration of the web in sub- and super-critical speeds. This method is in fact the Levy-type solution of the equation of motion in a stiffness matrix form. The exact vibration eigenvalues which are generally complex values, are the roots of stiffness matrix determinant. Since the terms of the determinant are complex transcendental functions of eigenvalues, classical eigenvalue solver can not be used. So a suitable algorithm is used here to extract eigenvalues in the two-dimensional plane of complex numbers. Using a numerical example, the reliability of the formulation and the solution procedure is shown. The free vibration eigenvalues is extracted for a range of axial speeds. Based on the results, flutter and divergence instabilities of the moving web are studied at supercritical speeds.
Succeeding in Business with Microsoft Office Access 2007: A Problem-Solving Approach, 1st edition
Authors: Sandra Cable
Venue:  (2007)

Succeeding in Business with Microsoft Access 2007: A Problem-Solving Approach prepares users to analyze data and solve real-life business problems using Microsoft Access as a tool. Moving beyond a basic point and click focus, it challenges you to use critical thinking and analysis to find efficient and effective solutions to real-life situations.
Reflection symmetry detection to reduce the state space of Markovian models
Authors: Ruth Lamprecht, Peter Kemper
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

A model-based evaluation of a system's design often considers to what degree components need to be available multiple times in order to reach a desired level of availability, reliability or dependability. Multiple components of the same kind then lead to models with regular structures. In stochastic models, especially Markovian models, such regularities have been studied for a long time and are used to establish lumpability results that help to achieve a significant state space reduction and alleviate the effects of the infamous state space explosion problem. In this paper, we introduce a new procedure to identify and reduce Markovian models that are built in a compositional manner based on sharing state variables. This procedure can also detect symmetries based on reflection in spatial models where state variables can commute. The results extend existing work of Obal, McQuinn, and Sanders and will contribute to M&ouml;bius, a multi-paradigm, multi-solution framework for the model-based dependability and performance assessment of systems.
Semantic similarity based feature extraction from microarray expression data
Authors: Young-Rae Cho, Aidong Zhang, Xian Xu
Venue: International Journal of Data Mining and Bioinformatics (2009)

Previous studies have proven that it is feasible to build sample classifiers using gene expression profiles. To build an effective sample classifier, dimension reduction process is necessary since classic pattern recognition algorithms do not work well in high dimensional space. In this paper, we present a novel feature extraction algorithm by integrating microarray expression data with Gene Ontology (GO). Applying semantic similarity measures, we identify the groups of genes, called virtual genes, which potentially interact with each other for a biological function. The correlation in expressions of virtual genes is used to classify samples. For colon cancer data, this approach significantly improved the classification accuracy by more than 10&#37;.
Some challenges facing software engineers developing software for scientists
Authors: Judith Segal
Venue: Proceedings of the 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering (2009)

In this paper, I discuss two types of challenges facing software engineers as they develop software for scientists. The first type is those challenges that arise from the experience that scientists might have of developing their own software. From this experience, they internalise a model of software development but may not realise the contextual factors which make such a model successful. They thus have expectations and assumptions which prove challenging to software engineers. The second type is those challenges which, while not unique to the development of software for scientists, have especial significance in the context of such development. These include the challenges of ensuring effective user engagement and of developing software for a community.
Positioning a point target in an aerial image
Authors: Shen-Chi Tien, Tsorng-Lin Chia
Venue: Digital Signal Processing (2009)

This paper provides a novel method utilizing the recognized and verified support objects and the geometrical relationships to position a point target in an aerial image. The method benefits from requiring a small model database, representing objects simply, and shortening the time of both searching and matching the database data. Features of the support objects under translation, rotation and scale transformations as well as perspective projection are invariants. Moreover, a weighted LMedS algorithm is used to reduce noise influence and improve the accuracy of the positioning result. The experimental results demonstrate that the proposed method is effective and robust to position a point target in an aerial image.
A Context-Aware Approach for Integrating Semantic Web Technologies onto Mobile Devices
Authors: Stefan Zander
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Semantic Web technologies such as RDF are usually incorporated in the infrastructure of desktop and web applications and can currently not be entirely deployed on mobile devices. Therefore, the unique opportunities and novel features the Semantic Web offers are not amenable in mobile application scenarios, in which context and context-awareness is essential. In this thesis, we propose a context-sensitive Semantic Web framework for mobile devices by which contextual data can be processed semantically and integrated with data sets and services from other communities. Mobile devices augmented with those capabilities can operate autonomously and independent in different environments.
Dividend payments in the classical risk model under absolute ruin with debit interest
Authors: Chunwei Wang, Chuancun Yin
Venue: Applied Stochastic Models in Business and Industry (2009)

This paper attempts to study the dividend payments in a compound Poisson surplus process with debit interest. Dividends are paid to the shareholders according to a barrier strategy. An alternative assumption is that business can go on after ruin, as long as it is profitable. When the surplus is negative, a debit interest is applied. At first, we obtain the integro-differential equations satisfied by the moment-generating function and moments of the discounted dividend payments and we also prove the continuous property of them at zero. Then, applying these results, we get the explicit expressions of the moment-generating function and moments of the discounted dividend payments for exponential claims. Furthermore, we discuss the optimal dividend barrier when the claim sizes have a common exponential distribution. Finally, we give the numerical examples for exponential claims and Erlang (2) claims. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Automatic vectorization using dynamic compilation and tree pattern matching technique in Jikes RVM
Authors: Sara El-Shobaky, Ahmed El-Mahdy, Ahmed El-Nahas
Venue: Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems (2009)

Modern processors incorporate SIMD instructions to improve the performance of multimedia applications. Vectorizing compilers are therefore sought to efficiently generate SIMD instructions. With the existence of different families of SIMD instruction sets, the task of compiler writers is more complex. Moreover virtual machines, such as JVMs, are currently widely used for increasing the portability of programs across different platforms; performing SIMDization on these virtual machines would further require 'fast' compilation. This paper selects an efficient retargetable compilation technique, based on tree-pattern matching, which generates efficient SIMD code on static compilers, and studies its utility on the Jikes RVM. The paper extends BURS system used in Jikes optimizing compiler accordingly, and adds new rules for manipulating subword data for the IA-32 architecture. Initial experimental results show an overall speedup at runtime despite dynamic compilation overheads.
Sparse relational data sets: issues and an application
Authors: Jeffrey F. Naughton
Venue:  (2008)

This dissertation comprises three parts. The first part presents a relational approach to building a workbench that supports extracting and processing structured data from unstructured data for querying, and that allows users to query using as much structured data as is currently available. The workbench provides basic operations that can be combined to process data in a "pay as you go" fashion, and a wide table to store the resulting sparse data set, in which most attributes are null for most documents. As a proof of concept, we conducted a case study on applying this relational workbench approach to support structured queries over Wikipedia. We present examples of incremental data processing with a series of operations and show that users can pose increasingly sophisticated queries over the results of these operations. Our conclusion from the case study is that while the relational workbench approach is promising and worth investigating, its success heavily relies on good relational database support for sparse data. Unfortunately, most relational database systems are not good at handling sparse data sets. The second part of the dissertation addresses some challenges presented when managing sparse data in relational database systems. With recent work showing that we can store sparse data efficiently by using interpreted storage, we show that storing a sparse data set in a single, wide table is an effective approach. For querying, we show that keyword search often provides "focused" results because most terms appear in few rows and columns. As for query evaluation, we show that sparse B-tree indexes allow a much broader index coverage over a sparse data set than the more common full B-tree indexes; moreover, we describe how to automatically infer from a sparse data set a "hidden schema," which we could use as a reference for building materialized views, a browsing directory, or query forms over the wide table. The last part of the dissertation addresses the problem of building structured queries over a relational database system without knowing SQL and the database schema. We propose to combine keyword search and query forms to try to provide the best of both. We present a systematic approach to generate forms, and propose techniques to handle keyword queries having a mix of database constants and terms that appear on the forms. We consider grouping similar forms returned by keyword search to help users more easily find the right form. Our real-life user study shows that our keyword search implementations are efficient and generally effective, and that grouping forms is an important post-processing step of keyword search to lead users to the right forms.
Bootstrapping Accurate Learner Models from Electronic Portfolios
Authors: Zinan Guo, Jim Greer
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

This paper presents a way to use electronic portfolios (e-portfolios) to initialize learner models for adaptive learning environments. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system to test whether accurate learner models can be built from e-portfolios and to determine whether this approach can be beneficial for reflective learning. EP-LM supports e-portfolio browsing, claiming skill levels on certain concepts, and evidencing the claims using artifacts included in the e-portfolio. Experimental results show that accurate learner models can be created through this approach and that reflective and personalized learning can result.
Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency
Authors: Jochen W. Klingelhoefer, Loukas Moutsianas, Chris Holmes
Venue: Bioinformatics (2009)

Motivation: Short interfering RNA (siRNA)-induced RNA interference is an endogenous pathway in sequence-specific gene silencing. The potency of different siRNAs to inhibit a common target varies greatly and features affecting inhibition are of high current interest. The limited success in predicting siRNA potency being reported so far could originate in the small number and the heterogeneity of available datasets in addition to the knowledge-driven, empirical basis on which features thought to be affecting siRNA potency are often chosen. We attempt to overcome these problems by first constructing a meta-dataset of 6483 publicly available siRNAs (targeting mammalian mRNA), the largest to date, and then applying a Bayesian analysis which accommodates feature set uncertainty. A stochastic logistic regression-based algorithm is designed to explore a vast model space of 497 compositional, structural and thermodynamic features, identifying associations with siRNA potency. Results: Our algorithm reveals a number of features associated with siRNA potency that are, to the best of our knowledge, either under reported in literature, such as anti-sense 5′ -3′ motif ‘UCU’, or not reported at all, such as the anti-sense 5′ -3′ motif ‘ACGA’. These findings should aid in improving future siRNA potency predictions and might offer further insights into the working of the RNA-induced silencing complex (RISC). Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.