The Experts below are selected from a list of 43587 Experts worldwide ranked by ideXlab platform
Timo Soininen - One of the best experts on this subject based on the ideXlab platform.
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towards intelligent support for managing evolution of configurable software product families
Lecture Notes in Computer Science, 2003Co-Authors: Tero Kojo, Tomi Mannisto, Timo SoininenAbstract:Software product families are a means for increasing the efficiency of software development. We propose a conceptualisation for modelling the evolution and variability of configurable software product families. We describe a first prototype of an intelligent tool that allows modelling a software product family on the basis of the conceptualisation and supports the user in interactively producing correct Configurations with respect to the model. The implementation is based on an existing general purpose configurator and thus is not application domain specific. We use the Debian Familiar Linux package Configuration Task over many releases and package versions as an example. Preliminary results show that the conceptualisation can be used to model evolution of such a software product family relatively easily and the implementation performs acceptably.
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representing Configuration knowledge with weight constraint rules
Answer Set Programming, 2001Co-Authors: Timo Soininen, Juha Tiihonen, Reijo SulonenAbstract:The paper demonstrates that product Configuration applications fit naturally the framework of answer set programming. It is shown that product Configuration knowledge can be represented systematically and compactly using a logic program type rule language such that the answers of a Configuration Task, the Configurations, correspond to the models of the rule representation.. The paper pr.es.~nts such a"}ule-bised forrealization of a unified Configuration oni~o]ogy using a" weight constraint rule language. The language extends normal logic programs with cardinality and weight constraints which leads to a compact and simple formalization. The complexity of the Configuration Task defined by the formalization is shown to be NP-complete.
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unified Configuration knowledge representation using weight constraint rules
2000Co-Authors: Timo Soininen, Juha Tiihonen, Reijo SulonenAbstract:In this paper we present an approach to formally defining different conceptualizations of Configuration knowledge, modeling Configuration knowledge, and implementing the Configuration Task on the basis of the formalization. The approach is based on a weight constraint rule language and its efficient implementation designed for representing different aspects of Configuration knowledge uniformly and compactly. The use of the language to represent Configuration knowledge is demonstrated through formalizing a unified Configuration ontology. The language allows a compact and simple formalization. The complexity of the Configuration Task defined by the formalization is shown to be NP-complete.
Nancy Kanwisher - One of the best experts on this subject based on the ideXlab platform.
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face perception domain specific not process specific
Neuron, 2004Co-Authors: Galit Yovel, Nancy KanwisherAbstract:Evidence that face perception is mediated by special cognitive and neural mechanisms comes from fMRI studies of the fusiform face area (FFA) and behavioral studies of the face inversion effect. Here, we used these two methods to ask whether face perception mechanisms are stimulus specific, process specific, or both. Subjects discriminated pairs of upright or inverted faces or house stimuli that differed in either the spatial distance among parts (Configuration) or the shape of the parts. The FFA showed a much higher response to faces than to houses, but no preference for the Configuration Task over the part Task. Similarly, the behavioral inversion effect was as large in the part Task as the Configuration Task for faces, but absent in both part and Configuration Tasks for houses. These findings indicate that face perception mechanisms are not process specific for parts or Configuration but are domain specific for face stimuli per se.
Reijo Sulonen - One of the best experts on this subject based on the ideXlab platform.
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representing Configuration knowledge with weight constraint rules
Answer Set Programming, 2001Co-Authors: Timo Soininen, Juha Tiihonen, Reijo SulonenAbstract:The paper demonstrates that product Configuration applications fit naturally the framework of answer set programming. It is shown that product Configuration knowledge can be represented systematically and compactly using a logic program type rule language such that the answers of a Configuration Task, the Configurations, correspond to the models of the rule representation.. The paper pr.es.~nts such a"}ule-bised forrealization of a unified Configuration oni~o]ogy using a" weight constraint rule language. The language extends normal logic programs with cardinality and weight constraints which leads to a compact and simple formalization. The complexity of the Configuration Task defined by the formalization is shown to be NP-complete.
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unified Configuration knowledge representation using weight constraint rules
2000Co-Authors: Timo Soininen, Juha Tiihonen, Reijo SulonenAbstract:In this paper we present an approach to formally defining different conceptualizations of Configuration knowledge, modeling Configuration knowledge, and implementing the Configuration Task on the basis of the formalization. The approach is based on a weight constraint rule language and its efficient implementation designed for representing different aspects of Configuration knowledge uniformly and compactly. The use of the language to represent Configuration knowledge is demonstrated through formalizing a unified Configuration ontology. The language allows a compact and simple formalization. The complexity of the Configuration Task defined by the formalization is shown to be NP-complete.
Galit Yovel - One of the best experts on this subject based on the ideXlab platform.
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face perception domain specific not process specific
Neuron, 2004Co-Authors: Galit Yovel, Nancy KanwisherAbstract:Evidence that face perception is mediated by special cognitive and neural mechanisms comes from fMRI studies of the fusiform face area (FFA) and behavioral studies of the face inversion effect. Here, we used these two methods to ask whether face perception mechanisms are stimulus specific, process specific, or both. Subjects discriminated pairs of upright or inverted faces or house stimuli that differed in either the spatial distance among parts (Configuration) or the shape of the parts. The FFA showed a much higher response to faces than to houses, but no preference for the Configuration Task over the part Task. Similarly, the behavioral inversion effect was as large in the part Task as the Configuration Task for faces, but absent in both part and Configuration Tasks for houses. These findings indicate that face perception mechanisms are not process specific for parts or Configuration but are domain specific for face stimuli per se.
Alex Pentland - One of the best experts on this subject based on the ideXlab platform.
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mining face to face interaction networks using sociometric badges predicting productivity in an it Configuration Task
International Conference on Information Systems, 2008Co-Authors: Benjamin N Waber, Sinan Aral, Erik Brynjolfsson, Alex PentlandAbstract:We integrate social network theories and information richness theory to understand which social structures are associated with effective knowledge transfer and higher productivity in face-to-face communication networks. Using novel data collection tools and methodologies, we record precise data on face-to-face interaction networks, tonal conversational variation and physical proximity among a group of IT Configuration specialists over one month. By linking these data to detailed performance and productivity metrics we find that 1) network cohesion is associated with higher worker productivity, in contrast to previous findings for email data; 2) cohesion in face-to-face networks is associated with even higher performance during complex Tasks, suggesting that social cohesion complements information-rich communication media when Tasks are complex; 3) dominant structures for “latent” networks differ from “in-Task” networks; and 4) face-to-face networks have more explanatory power than physical-proximity networks. Our research opens new avenues for the measurement of face-to-face interaction and information worker productivity.
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mining face to face interaction networks using sociometric badges predicting productivity in an it Configuration Task
International Conference on Information Systems, 2008Co-Authors: Benjamin N Waber, Sinan Aral, Erik Brynjolfsson, Alex PentlandAbstract:Social network theories (e.g. Granovetter 1973, Burt 1992) and information richness theory (Daft & Lengel 1987) have both been used independently to understand knowledge transfer in information intensive work settings. Social network theories explain how network structures covary with the diffusion and distribution of information, but largely ignore characteristics of the communication channels (or media) through which information and knowledge are transferred. Information richness theory on the other hand focuses explicitly on the communication channel requirements for different types of knowledge transfer but ignores the population level topology through which information is transferred in a network. This paper aims to bridge these two sets of theories to understand what types of social structures are most conducive to transferring knowledge and improving work performance in face-to-face communication networks. Using a novel set of data collection tools, techniques and methodologies, we were able to record precise data on the face-to-face interaction networks, tonal conversational variation and physical proximity of a group of IT Configuration specialists over a one month period while they conducted their work. Linking these data to detailed performance and productivity metrics, we find four main results. First, the face-to-face communication networks of productive workers display very different topological structures compared to those discovered for email networks in previous research. In face-to-face networks, network cohesion is positively correlated with higher worker productivity, while the opposite is true in email communication. Second, network cohesion in face-to-face networks is associated with even higher work performance when executing complex Tasks. This result suggests that network cohesion may complement information-rich communication media for transferring the complex or tacit knowledge needed to complete complex Tasks. Third, the most effective network structures for latent social networks (those that characterize the network of available communication partners) differ from in-Task social networks (those that characterize the network of communication partners that are actualized during the execution of a particular Task). Finally, the effect of cohesion is much stronger in face-to-face networks than in physical proximity networks, demonstrating that information flows in actual conversations (rather than mere physical proximity) are driving our results. Our work bridges two influential bodies of research in order to contrast face-to-face network structure with network structure in electronic communication. We also contribute a novel set of tools and techniques for discovering and recording precise face-to-face interaction data in real world work settings.
