ppxscal/embedding_network huggingface dataset:
Authorisation and identity mapping services for the Open Science Grid
Authors: Markus Lorch, Dennis Kafura, Ian Fisk, Kate Keahey, Tim Freeman, Abhishek Singh Rana, Frank Würthwein
Venue: International Journal of High Performance Computing and Networking (2008)

An attribute-based authorisation infrastructure developed for the Open Science Grid (OSG) is presented. The infrastructure integrates existing identity-mapping and group-membership services using concepts prototyped in the PRIMA system. Authorisation scenarios for requests to compute and data resources are detailed. A new SAML obligated authorisation decision statement is introduced that attaches an XACML obligation to the authorisation decision. The use of obligations enables site-centralised, service-independent policy management. Authorisation decisions are enforced via a Workspace Service that creates constrained execution environments configured in accordance with the obligations and other attribute-based information. Finally, an experimental PRIMA authorisation service that extends and simplifies the infrastructure is described.
The development of a sub-population genetic algorithm II (SPGA II) for multi-objective combinatorial problems
Authors: Pei-Chann Chang, Shih-Hsin Chen
Venue: Applied Soft Computing (2009)

Previous research has shown that sub-population genetic algorithm is effective in solving the multi-objective combinatorial problems. Based on these pioneering efforts, this paper extends the SPGA algorithm with a global Pareto archive technique and a two-stage approach to solve the multi-objective problems. In the first stage, the areas next to the two single objectives are searched and solutions explored around these two extreme areas are reserved in the global archive for later evolutions. Then, in the second stage, larger searching areas except the middle area are further extended to explore the solution space in finding the near-optimal frontiers. Through extensive experimental results, SPGA II does outperform SPGA, NSGA II, and SPEA 2 in the parallel scheduling problems and knapsack problems; it shows that the approach improves the sub-population genetic algorithm significantly. It may be of interests for researchers in solving multi-objective combinatorial problems.
Negotiation in Semi-cooperative Agreement Problems
Authors: Elisabeth Crawford, Manuela Veloso
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02 (2008)

In this paper we introduce the Semi-Cooperative Extended Incremental Multiagent Agreement Problem with Preferences (SC-EIMAPP). In SC-EIMAPPs, variables arise over time. For each variable, a set of distributed agents gain utility for agreeing on an option to assign to the variable. We define semi-cooperative utility as an agent's privately owned preferences, discounted as negotiation time increases. SC-EIMAPPs reflect real world agreement problems, including meeting scheduling and task allocation.We analyze negotiation in SC-EIMAPPs theoretically. We note that agents necessarily \emph{reveal} information about their own preferences and constraints as they negotiate agreements. We show how agents can use this limited and noisy information to learn to negotiate more effectively. We demonstrate our results experimentally.
The non-existence of a regular exceptional family of elements. A necessary and sufficient condition. Applications to complementarity theory
Authors: G. Isac, S. Z. Németh
Venue: Journal of Global Optimization (2008)

This paper is the second part of our recent work [Isac and Németh, J Optim Theory Appl (forthcoming)]. Our goal is now to present some new results related to the non-existence of a regular exceptional family of elements (REFE) for a mapping and to show how can they be applied to complementarity theory.
Constructing an atlas for the diffeomorphism group of a compact manifold with boundary, with application to the analysis of image registrations
Authors: Carole J. Twining, Stephen Marsland
Venue: Journal of Computational and Applied Mathematics (2008)

This paper considers the problem of defining a parameterization (chart) on the group of diffeomorphisms with compact support, motivated primarily by a problem in image registration, where diffeomorphic warps are used to align images. Constructing a chart on the diffeomorphism group will enable the quantitative analysis of these warps to discover the normal and abnormal variation of structures in a population. We construct a chart for particular choices of boundary conditions on the space on which the diffeomorphism acts, and for a particular class of metrics on the diffeomorphism group, which define a class of diffeomorphic interpolating splines. The geodesic equation is computed for this class of metrics, and we show how it can be solved in the spline representation. Furthermore, we demonstrate that the spline representation generates submanifolds of the diffeomorphism group, and we study this mapping. Explicit computational examples are included, showing how this chart can be constructed in practice, and that the use of the geodesic distance allows better classification of variation than those obtained using just a Euclidean metric on the space of warps.
A narrative approach for speech signal based MMSE estimation using quantum parameters
Authors: S. Sasikumar, S. Karthikeyan, M. Suganthi, M. Madheswaran
Venue: WSEAS Transactions on Signal Processing (2007)

In this paper, the performance of different estimators in estimating the speech signal through Quantum parameters can be analyzed. The main objective is to estimate the speech signal by a set of linear and Non-linear estimators that are proposed to be efficient in performance. The Minimax mean square error estimator is designed to minimize the worst-case MSE. In an estimation context, the objective typically is to minimize the size of the estimation error, rather than that of the data error as a cause, in many practical scenarios the least-squares estimator is known to result in a large MSE. A comparative analysis between MMSE estimator with other linear and nonlinear estimators can be performed. The analysis proved that the MMSE estimator can outperform both from linear and nonlinear estimator.
Imbalanced text classification: A term weighting approach
Authors: Ying Liu, Han Tong Loh, Aixin Sun
Venue: Expert Systems with Applications: An International Journal (2009)

The natural distribution of textual data used in text classification is often imbalanced. Categories with fewer examples are under-represented and their classifiers often perform far below satisfactory. We tackle this problem using a simple probability based term weighting scheme to better distinguish documents in minor categories. This new scheme directly utilizes two critical information ratios, i.e. relevance indicators. Such relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study using both Support Vector Machines and Naive Bayes classifiers and extensive comparison with other classic weighting schemes over two benchmarking data sets, including Reuters-21578, shows significant improvement for minor categories, while the performance for major categories are not jeopardized. Our approach has suggested a simple and effective solution to boost the performance of text classification over skewed data sets.
Local bifurcations of critical periods for cubic Liénard equations with cubic damping
Authors: Lan Zou, Xingwu Chen, Weinian Zhang
Venue: Journal of Computational and Applied Mathematics (2008)

Continuing Chicone and Jacobs' work for planar Hamiltonian systems of Newton's type, in this paper we study the local bifurcation of critical periods near a nondegenerate center of the cubic Lienard equation with cubic damping and prove that at most 2 local critical periods can be produced from either a weak center of finite order or the linear isochronous center and that at most 1 local critical period can be produced from nonlinear isochronous centers.
Personalized E-Learning through Environment Design and Collaborative Activities
Authors: Felix Mödritscher, Fridolin Wild
Venue: Proceedings of the 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for Education and Work (2008)

Over the last century, many theoretical frameworks and technological solutions for personalized e-learning have emerged. The underlying models, however, are often based on the practice that domain experts develop an adaptation strategy to personalize content or parts of a learning platform, which leads to different problematic aspects decreasing the feasibility or utility of such approaches. After giving a brief overview of the historical development and basic concepts of personalized e-learning, we outline the shortcomings of the traditional `top-down, ex ante' models and present an alternative approach which deals with personal learning environments, web application mashups, learning activities and learner interactions, as well as pattern-based best practice sharing. Furthermore, a prototypic implementation for our `learner-driven, bottom-up' approach to personalized e-learning, namely the `Mash-UP Personal Learning Environment' (MUPPLE), is presented and discussed on the basis of a concrete scenario.
Towards de novo identification of metabolites by analyzing tandem mass spectra
Authors: Sebastian Böcker, Florian Rasche
Venue: Bioinformatics (2008)

Motivation: Mass spectrometry is among the most widely used technologies in proteomics and metabolomics. Being a highthroughput method, it produces large amounts of data that necessitates an automated analysis of the spectra. Clearly, database search methods for protein analysis can easily be adopted to analyze metabolite mass spectra. But for metabolites, de novo interpretation of spectra is even more important than for protein data, because metabolite spectra databases cover only a small fraction of naturally occurring metabolites: even the model plant Arabidopsis thaliana has a large number of enzymes whose substrates and products remain unknown. The field of bio-prospection searches biologically diverse areas for metabolites which might serve as pharmaceuticals. De novo identi.cation of metabolite mass spectra requires new concepts and methods since, unlike proteins, metabolites possess a non-linear molecular structure. Results: In this work, we introduce a method for fully automated de novo identification of metabolites from tandem mass spectra. Mass spectrometry data is usually assumed to be insufficient for identification of molecular structures, so we want to estimate the molecular formula of the unknown metabolite, a crucial step for its identification. The method first calculates all molecular formulas that explain the parent peak mass. Then, a graph is build where vertices correspond to molecular formulas of all peaks in the fragmentation mass spectra, whereas edges correspond to hypothetical fragmentation steps. Our algorithm afterwards calculates the maximum scoring subtree of this graph: each peak in the spectra must be scored at most once, so the subtree shall contain only one explanation per peak. Unfortunately, finding this subtree is NP-hard. We suggest three exact algorithms (including one fixedparameter tractable algorithm) as well as two heuristics to solve the problem. Tests on real mass spectra show that the FPT algorithm and the heuristics solve the problem suitably fast and provide excellent results: for all 32 test compounds the correct solution was among the top five suggestions, for 26 compounds the first suggestion of the exact algorithm was correct. Availability: http://www.bio.inf.uni-jena.de/tandemms Contact: florian.rasche@minet.uni-jena.de
An empirical study of the effect of time constraints on the cost-benefits of regression testing
Authors: Hyunsook Do, Siavash Mirarab, Ladan Tahvildari, Gregg Rothermel
Venue: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (2008)

Regression testing is an expensive process used to validate modified software. Test case prioritization techniques improve the cost-effectiveness of regression testing by ordering test cases such that those that are more important are run earlier in the testing process. Many prioritization techniques have been proposed and evidence shows that they can be beneficial. It has been suggested, however, that the time constraints that can be imposed on regression testing by various software development processes can strongly affect the behavior of prioritization techniques. Therefore, we conducted an experiment to assess the effects of time constraints on the costs and benefits of prioritization techniques. Our results show that time constraints can indeed play a significant role in determining both the cost-effectiveness of prioritization, and the relative cost-benefit tradeoffs among techniques, with important implications for the use of prioritization in practice.
Unsupervised Discovery of Coordinate Terms for Multiple Aspects from Search Engine Query Logs
Authors: Masashi Yamaguchi, Hiroaki Ohshima, Satoshi Oyama, Katsumi Tanaka
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

A method is described for discovering coordinate terms, such as "Honda'' and "Nissan,'' for a given term, such as "Toyota,'' as well as their common topic terms, from the query logs of a Web search engine. Coordinate terms are good candidates for use in making comparisons. A HITS-based algorithm is applied to a bipartite graph between coordinate term candidates and co-occurrence patterns to identify coordinate and topic terms. Spectral analysis is used to distinguish coordinate terms corresponding to different aspects of the search term. As a result, we can discover terms related to the terms in a search engine query that reflect the needs and interests of the user.
SLEUTH: Single-pubLisher attack dEtection Using correlaTion Hunting
Authors: Ahmed Metwally, Fatih Emekçi, Divyakant Agrawal, Amr El Abbadi
Venue: Proceedings of the VLDB Endowment (2008)

Several data management challenges arise in the context of Internet advertising networks, where Internet advertisers pay Internet publishers to display advertisements on their Web sites and drive traffic to the advertisers from surfers' clicks. Although advertisers can target appropriate market segments, the model allows dishonest publishers to defraud the advertisers by simulating fake traffic to their own sites to claim more revenue. This paper addresses the case of publishers launching fraud attacks from numerous machines, which is the most widespread scenario. The difficulty of uncovering these attacks is proportional to the number of machines and resources exploited by the fraudsters. In general, detecting this class of fraud entails solving a new data mining problem, which is finding correlations in multidimensional data. Since the dimensions have large cardinalities, the search space is huge, which has long allowed dishonest publishers to inflate their traffic, and deplete the advertisers' advertising budgets. We devise the approximate SLEUTH algorithms to solve the problem efficiently, and uncover single-publisher frauds. We demonstrate the effectiveness of SLEUTH both analytically and by reporting some of its results on the Fastclick network, where numerous fraudsters were discovered.
Video: Digital Communication & Production Workbook, 2 Workbook edition
Authors: Jim Stinson
Venue:  (2007)

Workbook to accompany the textbook. 240 pages with 24 sections and numerous activities.
Accounting for atmospheric boundary layer variability on flux estimation from RS observations
Authors: W. J. Timmermans, G. Bertoldi, J. D. Albertson, A. Olioso, Z. Su, A. S. M. Gieske
Venue: International Journal of Remote Sensing (2008)

A Large Eddy Simulation (LES) model is coupled to a remote-sensing-based SVAT that accounts for soil and vegetation in order to study the feedback effects between surface state and spatial variability in fluxes, through the induction of spatial variability in the lower atmosphere. As such, this study focuses on sensible heat flux exchange. The simulations indicated that changes are introduced to the flux distributions mainly at higher surface temperatures. A scale-dependent method is presented to account for feedback effects. This showed the most significant correlation between surface and air temperature at scales from 500 to 1000 m and modulated the spatial variance of sensible heat flux. This suggests that feedback effects act to limit the spatial variability in fluxes, and ignoring them will cause the largest errors at the extremes.
Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China
Authors: W. Sun, P. -X. Wang, S. -Y. Zhang, D. -H. Zhu, J. -M. Liu, J. -H. Chen, H. -S. Yang
Venue: International Journal of Remote Sensing (2008)

The aim of this study was to develop and validate a method of determining the warm and cold edges of the Vegetation Temperature Condition Index (VTCI) drought monitoring approach, and to analyse the time series profiles of the VTCI in croplands under both rainfed and irrigated conditions. The linear correlation coefficients between the VTCI and the cumulative precipitation at one or two periods of 10-day intervals are the highest. There are significant linear correlations between the VTCI and soil moisture at the 0-10-cm layer for each 10-day interval during the winter wheat growing seasons. These results indicate that the VTCI is an effective approach for monitoring drought occurrence after the crops turn green. The time series analysis of the VTCI in the 10-day intervals shows that the VTCI profiles are different for irrigated and rainfed conditions. The time series VTCI values under rainfed conditions have a good response to recent precipitation, while those under irrigated conditions have less agreement with recent precipitation due to irrigation practices. The results show that the VTCI is a better indicator of droughts than indices developed from precipitation data.
Markov model plus k-word distributions
Authors: Qi Dai, Yanchun Yang, Tianming Wang
Venue: Bioinformatics (2008)

Motivation: Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r. Results: The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient. Availability: The software, data and supplement material are freely available at http://math.dlut.edu.cn/daiqi/mplusd.html. Contact: daiailiu2004@yahoo.com.cn Supplementary information:Supplementary data are available at Bioinformatics online.
Latent social structure in open source projects
Authors: Christian Bird, David Pattison, Raissa D'Souza, Vladimir Filkov, Premkumar Devanbu
Venue: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (2008)

Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational "cathedrals" are to be contrasted with the "bazaar-like" nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. Still, in large, complex, successful, OSS projects, we do expect that subcommunities will form spontaneously within the developer teams. Studying these subcommunities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could well hold important lessons for how commercial software teams might be organized. Building on known well-established techniques for detecting community structure in complex networks, we extract and study latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed spontaneously arise within these projects as the projects evolve. These subcommunities manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour.
Affect as Information about Users' Attitudes to Conversational Agents
Authors: Michal Ptaszynski, Pawel Dybala, Shinsuke Higuchi, Rafal Rzepka, Kenji Araki
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

This paper presents a novel method for automatic evaluation of conversational agents. In the method, information about users’ attitudes and sentiments to conversational agents and their performance are achieved by analyzing their general emotional engagement in the conversation and specific affective states, and interpreting them using psychological reasoning of Affect-as-Information. In the evaluation experiment the users’ attitudes to two Japanese-speaking conversational agents were checked simultaneously in a survey and using a system constructed on the proposed method. The results returned by the system revealed similar tendencies as the survey. Therefore the method is applicable as a mean of evaluation for Japanese-speaking conversational agents.
Balancing the power of multimedia information retrieval and usability in designing interactive tv
Authors: Hyowon Lee, Paul Ferguson, Cathal Gurrin, Alan F. Smeaton, Noel E. O'Connor, Heeseon Park
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

Steady progress in the field of multimedia information retrieval (MMIR) promises a useful set of tools that could provide new usage scenarios and features to enhance the user experience in today's digital media applications. In the interactive TV domain, the simplicity of interaction is more crucial than in any other digital media domain and ultimately determines the success or otherwise of any new applications. Thus when integrating emerging tools like MMIR into interactive TV, the increase in interface complexity and sophistication resulting from these features can easily reduce its actual usability. In this paper we describe a design strategy we developed as a result of our efforts in balancing the power of emerging multimedia information retrieval techniques and maintaining the simplicity of the interface in interactive TV. By providing multiple levels of interface sophistication in increasing order as a viewer repeatedly presses the same button on their remote control, we provide a layered interface that can accommodate viewers requiring varying degrees of power and simplicity. A series of screen shots from the system we have actually developed and built illustrates how this is achieved.