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The physical or engineering assets within modern industries have consistently been considered as the core elements of significant value that constitute the backbone for success and overall growth. Ensuring the optimal performance of an asset is a crucial a ...
We show how nonlinear embedding algorithms popular for use with "shallow" semi-supervised learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of t ...
Today, enterprise applications provide large amounts of data and finding the right information on time for a given purpose is often a challenge. In these environments, users do not know what information is important, why it is important and finally, how to ...
The discovery of web documents about certain topics is an important task for web-based applications including web document retrieval, opinion mining and knowledge extraction. In this paper, we propose an agent-based focused crawling framework able to retri ...
We propose a novel method to automatically detect and extract the video modality of the sound sources that are present in a scene. For this purpose, we first assess the synchrony between the moving objects captured with a video camera and the sounds record ...
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Relevant and readily available information is a crucial basis for decision making, problem solving or performing knowledge-intensive work. The motivation for this thesis lays in the fact that in certain situations not all information provided by an applica ...
Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this dat ...
Many electricity suppliers around the world are deploying smart meters to gather fine-grained spatiotemporal consumption data and to effectively manage the collective demand of their consumer base. In this paper, we introduce a structured framework and a d ...
The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the communi ...
Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools prov ...