WisPaper: In-depth AI Paper Analysis & Neural Architecture Deconstruction

Studying the Influence of Social Media Use on Sales Performance: The Role of Relational Mediators

4379_What to track on the Twitter streaming API a knapsack bandits approach to dynamically update the search terms.

Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

Academic performance of university students and its relation with employment

Secure friend discovery in mobile social networks

PrivGeoCrowd: A toolbox for studying private spatial Crowdsourcing

Towards model driven crowdsourcing: First experiments, methodology and transformation

Evolution, Structure and Users' Attachment Behavior in Enterprise Social Networks

A new fingerprinting scheme using social network analysis for majority attack

Hit-or-Wait: Coordinating Opportunistic Low-effort Contributions to Achieve Global Outcomes in On-the-go Crowdsourcing

Deep Learning and Cultural Heritage: The CEPROQHA Project Case Study

Pruning Social Networks Using Structural Properties and Descriptive Attributes

Dependability of social network groups

Finding disjoint dense clubs in a social network

ACP: An Efficient User Location Privacy Preserving Protocol for Opportunistic Mobile Social Networks

4394_Image4Act Online Social Media Image Processing for Disaster Response.

4395_The Human Element in Social Networking.

Experimental Study on Predictive Modeling in the Gamification Marketing Application

4398_A Family of Z-Source Matrix Converters.

International Journal of Human - Computer Studies

An incentive scheme based on heterogeneous belief values for crowd sensing in mobile social networks

Reciprocal and Heterogeneous Link Prediction in Social Networks

Pay It Backward: Per-Task Payments on Crowdsourcing Platforms Reduce Productivity

4403_Truthful incentives in crowdsourcing tasks using regret minimization mechanisms.

An approach to providing a user of a “social folksonomy” with recommendations of similar users and potentially interesting resources

Abstract-Several studies have demonstrated that pathologic movement changes in knee osteoarthritis (OA) may contribute to disease progression. The aim of this study was to investigate the association between movement changes during stair ascent and pain, radiographic severity, and prognosis of knee OA in the elderly women using machine learning (ML) over a seven-year follow-up period. Eighteen elderly female patients with knee OA and 20 healthy controls were enrolled. Kinematic data for stair ascent were obtained using a 3D-motion analysis system at baseline. Kinematic factors were analyzed based on one of the popular ML methods, support vector machines (SVM). SVM was used to search kinematic predictors associated with pain, radiographic severity of knee OA, and unfavorable outcomes, which were defined as persistent knee pain as reported at the seven-year follow-up or as having undergone total knee replacement during the follow-up period. Six patients (46.2%) had unfavorable outcomes at the seven-year follow-up. SVM showed accuracy of detection of knee OA (97.4%), prediction of pain (83.3%), radiographic severity (83.3%), and unfavorable outcomes (69.2%). The predictors with SVM included the time of stair ascent, maximal anterior pelvis tilting, knee flexion at initial foot contact, and ankle dorsiflexion at initial foot contact. The interpretation of movement during stair ascent using ML may be helpful for physicians not only in detecting knee OA, but also in evaluating pain and radiographic severity

Design of a robotic agent that measures smile and facing behavior of children with Autism Spectrum Disorder

Towards understanding traveler behavior in Location-based Social Networks

A New Paradigm for the Study of Corruption in Different Cultures

CADIVa: cooperative and adaptive decentralized identity validation model for social networks

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