8,503 research outputs found
The Semantic Web Paradigm for a Real-Time Agent Control (Part I)
For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions, is the actual task for the distributed IT community. The real power of Intelligent Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The first part of this paper is an introductory of Semantic Web properties, and summarises agent characteristics and their actual importance in digital economy. The second part presents the predictability of a multiagent system used in a learning process for a control problem.Semantic Web, agents, fuzzy knowledge, evolutionary computing
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Neural network image reconstruction for magnetic particle imaging
We investigate neural network image reconstruction for magnetic particle
imaging. The network performance depends strongly on the convolution effects of
the spectrum input data. The larger convolution effect appearing at a
relatively smaller nanoparticle size obstructs the network training. The
trained single-layer network reveals the weighting matrix consisted of a basis
vector in the form of Chebyshev polynomials of the second kind. The weighting
matrix corresponds to an inverse system matrix, where an incoherency of basis
vectors due to a low convolution effects as well as a nonlinear activation
function plays a crucial role in retrieving the matrix elements. Test images
are well reconstructed through trained networks having an inverse kernel
matrix. We also confirm that a multi-layer network with one hidden layer
improves the performance. The architecture of a neural network overcoming the
low incoherence of the inverse kernel through the classification property will
become a better tool for image reconstruction.Comment: 9 pages, 11 figure
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Beyond TREC's filtering track
Following the withdrawal of the filtering track from the latest TREC conferences, there is a niche for new evaluation standards. Towards this end, we suggest, based on variations of TREC's routing subtask, two new evaluation methodologies. The first can be used for evaluating single, multi-topic profiles and the second for testing the ability of a multi-topic profile to adapt to both modest variations and radical drifts in user interests
Semantic Image Retrieval via Active Grounding of Visual Situations
We describe a novel architecture for semantic image retrieval---in
particular, retrieval of instances of visual situations. Visual situations are
concepts such as "a boxing match," "walking the dog," "a crowd waiting for a
bus," or "a game of ping-pong," whose instantiations in images are linked more
by their common spatial and semantic structure than by low-level visual
similarity. Given a query situation description, our architecture---called
Situate---learns models capturing the visual features of expected objects as
well the expected spatial configuration of relationships among objects. Given a
new image, Situate uses these models in an attempt to ground (i.e., to create a
bounding box locating) each expected component of the situation in the image
via an active search procedure. Situate uses the resulting grounding to compute
a score indicating the degree to which the new image is judged to contain an
instance of the situation. Such scores can be used to rank images in a
collection as part of a retrieval system. In the preliminary study described
here, we demonstrate the promise of this system by comparing Situate's
performance with that of two baseline methods, as well as with a related
semantic image-retrieval system based on "scene graphs.
An Intelligent Multi-Agent Recommender System for Human Capacity Building
This paper presents a Multi-Agent approach to the problem of recommending
training courses to engineering professionals. The recommendation system is
built as a proof of concept and limited to the electrical and mechanical
engineering disciplines. Through user modelling and data collection from a
survey, collaborative filtering recommendation is implemented using intelligent
agents. The agents work together in recommending meaningful training courses
and updating the course information. The system uses a users profile and
keywords from courses to rank courses. A ranking accuracy for courses of 90% is
achieved while flexibility is achieved using an agent that retrieves
information autonomously using data mining techniques from websites. This
manner of recommendation is scalable and adaptable. Further improvements can be
made using clustering and recording user feedback.Comment: Proceedings of the 14th IEEE Mediterranean Electrotechnical
Conference, 2008, pages 909 to 91
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