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Unsupervised Language-Independent Name Translation Mining from
"... The automatic generation of entity profiles from unstructured text, such as Knowledge Base Population, if applied in a multi-lingual setting, generates the need to align such profiles from multiple languages in an unsupervised manner. This paper describes an unsupervised and language-independent app ..."
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Cited by 11 (4 self)
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The automatic generation of entity profiles from unstructured text, such as Knowledge Base Population, if applied in a multi-lingual setting, generates the need to align such profiles from multiple languages in an unsupervised manner. This paper describes an unsupervised and language
R.: Inferring user’s information context from user profiles and concept hierarchies
- In: Proceedings of the 2004 Meeting of the International Federation of Classification Societies, IFCS 2004
, 2004
"... Abstract The critical elements that make up a user’s information context include the user profiles that reveal long-term interests and trends, the short-term information need as might be expressed in a query, and the semantic knowledge about the domain being investigated. The next generation of inte ..."
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Cited by 1 (1 self)
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the problem in the context of our client-side Web agent ARCH (Adaptive Retrieval based on Concept Hierarchies). In ARCH, the user profiles are generated using an unsupervised document clustering technique. These profiles, in turn, are used to automatically learn the semantic context of user’s information need
: Clustering-based Online
"... Wessbas is an approach for workload extraction and specification for session-based application systems. It comprises i.) the Wessbas-DSL, a domain-specific language for system- and tool-agnostic modeling of probabilistic session-based workloads; ii.) support for automatic extraction of Wessbas-DSL i ..."
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considering information about when the sessions were executed by the customers. Goal The goals of this project is to investigate how the Wessbas approach can be extended to support online characterization of navigational profiles, which is, e.g., useful to analyze and predict the evolution of workloads over
Nonparametric hierarchical Bayesian model for functional brain parcellation
- Proceedings of MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis. IEEE
"... We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary act ..."
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Cited by 4 (1 self)
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We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary
QOS WITH RELIABILITY AND SCALABILITY IN ADAPTIVE SERVICE-BASED SYSTEMS
"... Service-based systems that are dynamically composed at runtime to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be manage ..."
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that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically
Nonparametric Hierarchical Bayesian Model for Functional Brain
"... We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary act ..."
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We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary
Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh
- 2015 by the authors; licensee MDPI
"... IntroDuctIon An increasing amount of biological and medical research relies on single-cell imaging to obtain information about the phenotypic response of cells to a variety of chemical, mechanical and genetic perturbations. Although it is possible to distinguish obvious phenotypes by eye, computati ..."
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Cited by 2 (0 self)
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. They are either based on applying various filters to the pixels of the image (e.g., CellProfiler 24 ), on using machine-learning techniques to classify pixels as belonging to an object or to the background (e.g., Ilastik 25 ), or on including models of the imaged objects and the image-formation process (e
Results 1 - 10
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20
