27,299 research outputs found
Automatic Concept Discovery from Parallel Text and Visual Corpora
Humans connect language and vision to perceive the world. How to build a
similar connection for computers? One possible way is via visual concepts,
which are text terms that relate to visually discriminative entities. We
propose an automatic visual concept discovery algorithm using parallel text and
visual corpora; it filters text terms based on the visual discriminative power
of the associated images, and groups them into concepts using visual and
semantic similarities. We illustrate the applications of the discovered
concepts using bidirectional image and sentence retrieval task and image
tagging task, and show that the discovered concepts not only outperform several
large sets of manually selected concepts significantly, but also achieves the
state-of-the-art performance in the retrieval task.Comment: To appear in ICCV 201
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
To accelerate software development, much research has been performed to help
people understand and reuse the huge amount of available code resources. Two
important tasks have been widely studied: code retrieval, which aims to
retrieve code snippets relevant to a given natural language query from a code
base, and code annotation, where the goal is to annotate a code snippet with a
natural language description. Despite their advancement in recent years, the
two tasks are mostly explored separately. In this work, we investigate a novel
perspective of Code annotation for Code retrieval (hence called `CoaCor'),
where a code annotation model is trained to generate a natural language
annotation that can represent the semantic meaning of a given code snippet and
can be leveraged by a code retrieval model to better distinguish relevant code
snippets from others. To this end, we propose an effective framework based on
reinforcement learning, which explicitly encourages the code annotation model
to generate annotations that can be used for the retrieval task. Through
extensive experiments, we show that code annotations generated by our framework
are much more detailed and more useful for code retrieval, and they can further
improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201
Bridging the Gap Between Traditional Metadata and the Requirements of an Academic SDI for Interdisciplinary Research
Metadata has long been understood as a fundamental component of any Spatial Data Infrastructure, providing information relating to discovery, evaluation and use of
datasets and describing their quality. Having good metadata about a dataset is fundamental to using it correctly and to understanding the implications of issues such as missing data or incorrect attribution on the results obtained for any analysis carried out.
Traditionally, spatial data was created by expert users (e.g. national mapping agencies), who created metadata for the data. Increasingly, however, data used in spatial analysis comes from multiple sources and could be captured or used by nonexpert users â for example academic researchers â many of whom are from nonâGIS disciplinary backgrounds, not familiar with metadata and perhaps working in geographically dispersed teams. This paper examines the applicability of metadata in this academic context, using a multiânational coastal/environmental project as a case study. The work to date highlights a number of suggestions for good practice, issues and research questions relevant to Academic SDI, particularly given the increased levels of research data sharing and reuse required by UK and EU funders
Integration via Meaning: Using the Semantic Web to deliver Web Services
Presented at the CRIS2002 Conference in Kassel.-- 9 pages.-- Contains: Conference paper (PDF) + PPT presentation.The major developments of the World Wide Web (WWW) in the last two years have been Web Services and the Semantic Web. The former allows the construction of distributed systems across the WWW by providing a lightweight middleware architecture. The latter provides an infrastructure for accessing resources on the WWW via their relationships with respect to conceptual descriptions. In this paper, I shall review the progress undertaken in each of these two areas. Further, I shall argue that in order for the aims of both the Semantic Web and the Web Services activities to be successful, then the Web Service architecture needs to be augmented by concepts and tools of the Semantic Web. This infrastructure will allow resource discovery, brokering and access to be enabled in a standardised, integrated and interoperable manner. Finally, I survey
the CLRC Information Technology R&D programme to show how it is contributing to the development of this future infrastructure
Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a userâs interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to
be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning
methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories.
We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that
proposes a new form of interaction between users and digital libraries, where the latter are adapted to users
and their surroundings
An analysis of the application of AI to the development of intelligent aids for flight crew tasks
This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research
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