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Offline Worker Selection for Real-Time Spatial Crowdsourcing Multi-Worker Tasks
Electrosense+: Crowdsourcing radio spectrum decoding using IoT receivers
A Multidimensional Dataset Based on Crowdsourcing for Analyzing and Detecting News Bias
9851_Stability analysis in dynamic social networks.
An efficient employment of internet of multimedia things in smart and future agriculture
Beyond interaction: meta-design and cultures of participation
Hierarchical Trust Level Evaluation for Pervasive Social Networking
Methods for User Profiling across Social Networks
An Ontology-Based Method to Link Database Integration and Data Mining within a Biomedical Distributed KDD
Personality profiles of global software developers
Inferring Social Networks from Outbreaks
Effective Clusterization of Political Tweets Using Kurtosis and Community Duration
9860_A Novel Classification Method From the Perspective of Fuzzy Social Networks Based on Physical and Implicit Style Features of Data.
Overview of the INEX 2010 Book Track: Scaling Up the Evaluation Using Crowdsourcing
Human emotion recognition using real 3D visual features from Gabor library
Sentiment Analysis and the Impact of Employee Satisfaction on Firm Earnings
Entropy-Based Graph Clustering: Application to Biological and Social Networks
Potentials of Emotionally Sensitive Applications Using Machine Learning
Exploring Social Cognition Related to Privacy Settings in SNS Usage
Agent-Based Simulation of Cultural Events Impact on Social Capital Dynamics
Predicting Unemployment with Machine Learning Based on Registry Data
Towards Profit Maximization for Online Social Network Providers
Inferring Personality of Online Gamers by Fusing Multiple-View Predictions
A Learning to Rank Framework for Developer Recommendation in Software Crowdsourcing
Dynamic Social Networks Generator Based on Modularity: DSNG-M
Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning
Prediction of Web User Behavior by Discovering Temporal Relational Rules from Web Log Data
Abstract-Dyslexia is a specific learning difficulty associated with brain capability in processing numbers and letters. Analysis of Electroencephalogram (EEG) could provide insight information on differences in brain processing. In this work, two machine learning techniques were applied to distinguish EEG signals of normal, poor and capable dyslexic children during writing word and non-word. The performance of knearest neighbour (KNN) with correlation distance function and extreme learning machine (ELM) with radial basis function (RBF) were compared. The performance of each classifier was determined using sensitivity, specificity and accuracy. It was found that ELM was capable of classifying the dyslexic children with 89% accuracy compared to KNN which is only 83%. These results showed that ELM is feasible and reliable in recognising normal, poor and capable dyslexic children through writing
WiCV at ECCV2018: The Fifth Women in Computer Vision Workshop
A formalized delegation model for multimedia social networks