52 Result(s)
within
English
Computer Science
Data Mining and Knowledge Discovery
Pattern Recognition
M. **gliang
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Chapter and Conference Paper
RF-Based Drone Detection with Deep Neural Network: Review and Case Study
Drones have been widely used in many application scenarios, such as logistics and on-demand instant delivery, surveillance, traffic monitoring, firefighting, photography, and recreation. On the other hand, the...
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Article
Open AccessPreference-based interactive multi-document summarisation
Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods requ...
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Chapter and Conference Paper
Exploring the Long Tail of Social Media Tags
There are millions of users who tag multimedia content, generating a large vocabulary of tags. Some tags are frequent, while other tags are rarely used following a long tail distribution. For frequent tags, mo...
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Chapter and Conference Paper
Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates
Two common ways of approaching atlas-based segmentation of brain MRI are (1) intensity-based modelling and (2) multi-atlas label fusion. Intensity-based methods are robust to registration errors but need disti...
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Chapter and Conference Paper
Hyperalignment of Multi-subject fMRI Data by Synchronized Projections
Group analysis of fMRI data via multivariate pattern methods requires accurate alignments between neuronal activities of different subjects in order to attain competitive inter-subject classification rates. Hy...
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Chapter and Conference Paper
A Stacking-Based Approach to Identify Translated Upstream Open Reading Frames in Arabidopsis Thaliana
Upstream open reading frames (uORFs) are open reading frames located within the 5’ UTR of an mRNA. It is believed that translated uORFs reduce the translational efficiency of the main coding region, and play a...
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Chapter and Conference Paper
Discriminative Interpolation for Classification of Functional Data
The modus operandi for machine learning is to represent data as feature vectors and then proceed with training algorithms that seek to optimally partition the feature space
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Chapter and Conference Paper
Element Stiffness Matrix Integration in Image-Based Cartesian Grid Finite Element Method
Patient specific Finite Element (FE) simulations are usually expensive. Time consuming geometry creation procedures are normally necessary to use standard FE meshing software, while direct pixel-based meshing ...
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Chapter and Conference Paper
An Adaptive Information Retrieval System for Efficient Web Searching
Stemming algorithms (stemmers) are used to convert the words to their root form (stem), this process is used in the pre-processing stage of the Information Retrieval Systems. The Stemmers affect the indexing t...
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Chapter and Conference Paper
Concept Recommendation System for Cloud Services Advertisement
Cloud computing is a major trend in Information Technology (IT), which has witnessed high adaption rate for cloud solutions. Software-as-a-Service (SaaS) providers compete to address nearly every business and ...
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Chapter and Conference Paper
Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine
Summarizing the evidence about medical interventions is an immense undertaking, in part because unstructured Portable Document Format (PDF) documents remain the main vehicle for disseminating scientific findin...
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Chapter and Conference Paper
Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity ha...
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Chapter and Conference Paper
Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder
We present an anatomically guided feature selection scheme for prediction of neurological disorders based on brain connectivity networks. Using anatomical information not only gives rise to an interpretable mo...
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Chapter and Conference Paper
Collections for Automatic Image Annotation and Photo Tag Recommendation
This paper highlights a number of problems which exist in the evaluation of existing image annotation and tag recommendation methods. Crucially, the collections used by these state-of-the-art methods contain a...
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Chapter and Conference Paper
Cosine Distance Metric Learning for Speaker Verification Using Large Margin Nearest Neighbor Method
In this paper, a novel cosine similarity metric learning based on large margin nearest neighborhood (LMNN) is proposed for an i-vector based speaker verification system. Generally, in an i-vector based speaker...
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Chapter and Conference Paper
At Odds with Curious Cats, Curious Robots Acquire Human-Like Intelligence
This work contributes to the development of a real-time intelligent system allowing to discover and to learn autonomously new knowledge about the surrounding world by semantic interaction with human. Based on ...
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Chapter and Conference Paper
Queries Based Workload Management System for the Cloud Environment
Workload management for concurrent queries is one of the challenging aspects of executing queries over the cloud computing environment. The core problem is to manage any unpredictable load imbalance with respe...
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Chapter and Conference Paper
Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model
We propose a probabilistic graphical model for predicting student attainment in web-based education. We empirically evaluate our model on a crowdsourced dataset with students and teachers; Teachers prepared le...
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Article
Crowdsourcing interactions: using crowdsourcing for evaluating interactive information retrieval systems
In the field of information retrieval (IR), researchers and practitioners are often faced with a demand for valid approaches to evaluate the performance of retrieval systems. The Cranfield experiment paradigm ...
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Chapter and Conference Paper
Decision-Theoretic Sparsification for Gaussian Process Preference Learning
We propose a decision-theoretic sparsification method for Gaussian process preference learning. This method overcomes the loss-insensitive nature of popular sparsification approaches such as the Informative Ve...
