In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Ever growing number of image documents available on the Internet continuously motivates research in better annotation models and more efficient retrieval methods. Formal knowledge representation of objects and events in pictures, their interaction as well as context complexity becomes no longer an option for a quality image repository, but a necessity. We present an ontologybased online image annotation tool WNtags and demonstrate its usefulness in several typical multimedia retrieval tasks using International Affective Picture System emotionally annotated image database. WNtags is built around WordNet lexical ontology but considers Suggested Upper Merged Ontology as the preferred labeling formalism. WNtags uses sets of weighted WordNet synsets as high-level image semantic descriptors and query matching is performed with word stemming and node distance metrics. We also elaborate our near future plans to expand image content description with induced affect as in stimuli for research of human emotion and attention.
[A]: WNtags: A Web-Based Tool For Image Labeling And Retrieval With Lexical Ontologies


[Q]: Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view representation learning technique with broad applicability in statistics and machine learning. Although there is a closed-form solution for the KCCA objective, it involves solving an N × N eigenvalue system where N is the training set size, making its computational requirements in both memory and time prohibitive for large-scale problems. Various approximation techniques have been developed for KCCA. A commonly used approach is to first transform the original inputs to an M -dimensional random feature space so that inner products in the feature space approximate kernel evaluations, and then apply linear CCA to the transformed inputs. In many applications, however, the dimensionality M of the random feature space may need to be very large in order to obtain a sufficiently good approximation; it then becomes challenging to perform the linear CCA step on the resulting very high-dimensional data matrices. We show how to use a stochastic optimization algorithm, recently proposed for linear CCA and its neuralnetwork extension, to further alleviate the computation requirements of approximate KCCA. This approach allows us to run approximate KCCA on a speech dataset with 1.4 million training samples and a random feature space of dimensionality M = 100000 on a typical workstation.
[A]: LARGE-SCALE APPROXIMATE KERNEL CANONICAL CORRELATION ANALYSIS


[Q]: Artifact-centric models for business processes recently raised a lot of attention as they manage to combine structural (i.e. data related) with dynamical (i.e. process related) aspects in a seamless way. This developed in parallel with declarative approaches for modelling processes, where activities are not burdened by over-specified constrains like in traditional process-centric approaches, but try to adapt the internal system to the humans involved and the input they receive. In this paper, we try to merge these two aspects by proposing a framework aimed at describing rich business domains through Description Logic-based ontologies, and where a set of actions allows the system to evolve by modifying such ontologies. We then propose an evolution of such framework which represents a viable and formal environment to develop decision making and planning techniques for DL-based artifactcentric business domains.
[A]:
Optimizations for Decision Making and Planning in Description Logic Based Dynamic Knowledge Bases