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Similarity Level Method Based Static Software Birthmarks
TLDR
We have proposed a method based similarity level software birthmark technique to detect copy of software. Expand
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Approach to design a compact reversible low power binary comparator
TLDR
Reversible logic has captured significant attention in recent time as reducing power consumption is the main concern of digital logic design. Expand
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A Multi-Task Architecture on Relevance-based Neural Query Translation
TLDR
We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. Expand
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Software Defect Prediction Using Feature Space Transformation
TLDR
We proposed a feature space transformation technique and classify the instances using Support Vector Machine (SVM) with its histogram intersection kernel. Expand
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SQuID: Semantic Similarity-Aware Query Intent Discovery
TLDR
We present SQuID, a system for Semantic similarity-aware Query Intent Discovery. Expand
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Term Relevance Feedback for Contextual Named Entity Retrieval
TLDR
We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approaches, and (2) that further gains are possible if the user indicates the relative importance of those terms. Expand
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User Similarity Computation for Collaborative Filtering Using Dynamic Implicit Trust
TLDR
We propose a new dynamic trust-based similarity approach for collaborative filtering based on implicit trust information of the users, which performs better than the existing trust based recommendation algorithms. Expand
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A New User Similarity Computation Method for Collaborative Filtering Using Artificial Neural Network
TLDR
A User-User Collaborative Filtering algorithm predicts the rating of a particular item for a given user based on the judgment of other users, who are similar to the given user. Expand
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Two-stage Cascaded Classifier for Purchase Prediction
TLDR
We have proposed a time efficient two-stage cascaded classifier for the prediction of buy sessions and purchased items within such sessions. Expand
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Named Entity Recognition with Extremely Limited Data
TLDR
We propose exploring named entity recognition as a search task, where the named entity class of interest is a query and entities of that class are the relevant "documents". Expand
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