80,004 research outputs found
Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System
This scientific paper delves into the problems related to the develop-ment of
intellectual data analysis system that could support decision making to manage
municipal power supply services. The management problems of mu-nicipal power
supply system have been specified taking into consideration modern tendencies
shown by new technologies that allow for an increase in the energy efficiency.
The analysis findings of the system problems related to the integrated
computer-aided control of the power supply for the city have been given. The
consideration was given to the hierarchy-level management decom-position model.
The objective task targeted at an increase in the energy effi-ciency to
minimize expenditures and energy losses during the generation and
transportation of energy carriers to the Consumer, the optimization of power
consumption at the prescribed level of the reliability of pipelines and
networks and the satisfaction of Consumers has been defined. To optimize the
support of the decision making a new approach to the monitoring of engineering
systems and technological processes related to the energy consumption and
transporta-tion using the technologies of geospatial analysis and Knowledge
Discovery in databases (KDD) has been proposed. The data acquisition for
analytical prob-lems is realized in the wireless heterogeneous medium, which
includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN
standard over the IEEE 802.15.4 standard and also the segments of the networks
of cellu-lar communications. JBoss Application Server is used as a server-based
plat-form for the operation of the tools used for the retrieval of data
collected from sensor nodes, PLC and energy consumption record devices. The KDD
tools are developed using Java Enterprise Edition platform and Spring and ORM
Hiber-nate technologies
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information
In this paper, we provide an approach to clustering relational matrices whose
entries correspond to either similarities or dissimilarities between objects.
Our approach is based on the value of information, a parameterized,
information-theoretic criterion that measures the change in costs associated
with changes in information. Optimizing the value of information yields a
deterministic annealing style of clustering with many benefits. For instance,
investigators avoid needing to a priori specify the number of clusters, as the
partitions naturally undergo phase changes, during the annealing process,
whereby the number of clusters changes in a data-driven fashion. The
global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
Intelligent indexing of crime scene photographs
The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently
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