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A Novel Approach Based On The Partially-Supervised Alignment Model For Mining Opinion Targets And Opinion Words From Online Reviews

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To extract and analyze opinions from online reviews, it is unsatisfactory to merely obtain the overall sentiment about a product. In most cases, customers expect to find fine-grained sentiments about an aspect or feature of a product that is reviewed. To this end, this abstract proposes a novel approach based on the partially-supervised alignment model, […]

The Word Alignment Model For Co-Extracting Opinion Targets And Opinion Words From Online Reviews

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From online reviews, customers can obtain first-hand assessments of product information and direct supervision of their purchase actions. Meanwhile, manufacturers can obtain immediate feedback and opportunities to improve the quality of their products in a timely fashion. Thus, mining opinions from online reviews has become an increasingly urgent activity and has attracted a great deal […]

DBP WITH THE APPLICATION IN HOTEL ENERGY MANAGEMENT

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For DBP, optimization problem is formulated with the objective of maximizing expected reward, which is received when the amount of energy saving satisfies the contract. For a general distribution of energy consumption, we give a general condition for the optimal bid and outline an algorithm to find the solution without numerical integration. Furthermore, for Gaussian […]

HOTEL ENERGY MANAGEMENT USING DEMAND BIDDING PROGRAM AND ITS APPLICATION

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Demand bidding program (DBP) is one type of demand response. DBP attracts large energy consumers to participate and encourages them to reduce their energy use by setting their own target. The customer is free to choose a bidding value in terms of the amount of energy reduction. If the actual amount of energy saving conforms […]

DISTRIBUTED DATA WITH INCREMENTAL DETECTION OF INCONSISTENCIES

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Errors in the data are typically detected as violations of constraints (data quality rules), such as functional dependencies (FDs), denial constraints, and conditional functional dependencies (CFDs). When the data is in a centralized database, it is known that two SQL queries suffice to detect its violations of a set of CFDs. This abstract investigates incremental […]

INCREMENTAL DETECTION IN DISTRIBUTED DATA

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Real life data is often dirty. To clean the data, efficient algorithms for detecting errors have to be in place. Errors in the data are typically detected as violations of constraints (data quality rules), such as functional dependencies (FDs), denial constraints, and conditional functional dependencies (CFDs). When the data is in a centralized database, it […]

SALIENCY DRIVEN IMAGE MULTISCALE NONLINEAR DIFFUSION FILTERING

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The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford […]

SPARSE BAYESIAN REGRESSION BASED PHOTOMETRIC STEREO FOR GENERAL DIFFUSE SURFACES

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            Photometric stereo involves estimating the surface normals of an object given appearance variations in multiple images taken under different lighting conditions. Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into two categories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertian structure for the inliers, and […]

SPARSE BAYESIAN REGRESSION BASED PHOTOMETRIC STEREO FOR GENERAL DIFFUSE SURFACES

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Photometric stereo involves estimating the surface normals of an object given appearance variations in multiple images taken under different lighting conditions. Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into two categories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertian structure for the inliers, and thus […]

FDM ALGORITHM BASED SECURE MINING OF ASSOCIATION RULES IN HORIZONTALLY DISTRIBUTED DATABASES

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Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al., which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset […]