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Top-k document retrieval, which returns highly relevant documents relative to a query, is an essential task for many applications. One of the promising index frameworks is built by FM-index and wavelet tree for supporting efficient top-k document retrieval. The index, however, has difficulty on handling document frequency (DF) at search time because indexed terms are all substrings of a document collection...
This paper presents a methodology to identify the connected feeder of high-usage customers in a primary distribution network. The proposed methodology considers voltage characteristics of radial distribution and actual measurements. Based on 15-minute intervals metering, cluster analysis is applied to categorize customer patterns on the basis of voltage correlation. Afterwards, support vector classification...
Transits are important in financial astrology because they affect human behavior. Human behavior is the driving force behind the markets. The strongest aspect to begin testing market outcomes is the conjunction. A conjunction of two positive planets should yield a positive market outcome. A conjunction of two negative planets should yield a positive outcome. Some classic examples of positive conjunctions...
The problem of training a classifier from a handful of positive examples, without having to supply class specific negatives is of great practical importance. The proposed approach to solving this problem builds on the idea of training LDA classifiers using only class specific foreground images and a large collection of unlabelled images, as described in [11]. While we adopt the LDA training methodology...
This paper presents an open recommender system to ease the entering barriers due to lack of sufficient background knowledge for small or new service providers. The proposed Open Preference and Feature recommender (OPF) uses user preference and item feature as the basis of recommendations, since the generality of preference and feature and therefore meets the needs of an open recommender system. In...
Feature selection is an essential technique used in high dimensional data. Basically, feature selection is focused on removing irrelevant features. But, removing redundant features is also equally important. We propose a novel feature subset selection algorithm based on the idea of consensus clustering. Our algorithm constructs a complete graph on feature space and partitions the graph using various...
This paper deals with the recognition of Telugu characters on palm leaf using statistical features. Handwritten character recognition has various applications in post offices, reading aids for blind, library automation and multimedia design. Palm leaf manuscripts contain religious texts and a host of subjects such as art, medicine, music, astrology, law and astronomy. There is an inherent 3D feature...
Reconstruction of displacement and strain fields in geomechanical structures from surface images is a challenging task. Digital Image Correlation (DIC) is a well known technique to achieve these tasks if deformation is continuous but it fails in the presence of discontinuities. This paper investigates the application of the DIC technique to displacement and strain field reconstruction in the presence...
With the evolution of various new types of application services for mobile devices, cellular operators have started providing multiple subscription plans to the mobile users. The plan subscribed determines the Quality of Service (QoS) to be provided to the user, and operators distinguish users as priority users and non-priority users, accordingly. To ensure better QoS to the priority users, necessary...
We present a simple and effective means for position estimation designed to be deployed in urban and dense multipath environments characteristic of 4G wireless networks. To address the multipath channel of such environments a fingerprinting scheme is proposed. One of the drawbacks to this class of methods is the large initial cost associated with establishing a database matrix. This issue is addressed...
Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data...
Social media data and other web-based network data are large and dynamic rendering the identification of structural changes in such systems a hard problem. Typically, online data is constantly streaming and results in data that is incomplete thus necessitating the need to understand the robustness of network metrics on partial or sampled network data. In this paper, we examine the effects of sampling...
In an aging society, a service robot will come into our life. It is important for a robot to identify an object specified by human speech from several objects. Human may request an object for the robot by its name, and/or color name etc. Although there are some research about the method for the object identification based on its name, the object identification based on its color is not discussed enough...
Improving branch prediction accuracy is essential in enabling high-performance processors to find more concurrency and to improve energy efficiency by reducing wrong path instruction execution, a paramount concern in today's power-constrained computing landscape. Branch prediction traditionally considers past branch outcomes as a linear, continuous bit stream through which it searches for patterns...
In this paper, we propose a new estimation method for the time difference of arrival (TDOA) between two microphones with improved accuracy by exploiting higher-order moments. In the proposed method analyzes the steered response power (SRP) of the observed signals after nonlinearly mapped onto a higher-dimensional space. Since the mapping operation enhances the linear independence between different...
Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power. While efforts have been made to study the crowdsourcing systems for labeling tasks such as classification, those for scoring tasks with continuous and correlative answers have not been well studied. In this paper, we propose two inference algorithms, MCE (Maximum Correlation...
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the challenge of exploiting correlations between labels. While in existing work these correlations are often modelled globally, in this paper we use the divide-and-conquer approach of decision trees which enables taking local decisions about how best to model label dependency. The resulting...
We proposed and evaluated an estimation method for the forced selection Japanese Diagnostic Rhyme Test (DRT). The proposed measure takes into account the forced selection manner of the DRT from a pair of rhyming words. The objective distance measure used here was based on the Articulation index Band Correlation (ABC), which showed favorable results for the English Modified Rhyme Test (MRT). The correlation...
Intrusion detection is used to protect the system from inside and outside attacks. Evolutionary algorithm has an important role in intrusion detection. Evolutionary algorithms are highly responsive for feature space reduction. The minimal number of features can improve the performance of an intrusion detection system. Thus we propose an intrusion detection system with various feature selection methods...
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