Not Every Couple Is a Pair: A Supervised Approach for Lifetime Collaborator Identification

While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar\u27s early profile. For this purpose, we propose a supervised approach by leveraging scholars\u27 local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration

Knowledge extraction from pointer movements and its application to detect uncertainty

This work was supported by the Doctoral Program NOVA I4H (Fundacao para a Ciencia e a Tecnologia) [grant PD/BDE/114561/2016].Pointer-tracking methods can capture a real-time trace at high spatio-temporal resolution of users' pointer interactions with a graphical user interface. This trace is potentially valuable for research on human-computer interaction (HCI) and for investigating perceptual, cognitive and affective processes during HCI. However, little research has reported spatio-temporal pointer features for the purpose of tracking pointer movements in on-line surveys. In two studies, we identified a set of pointer features and movement patterns and showed that these can be easily distinguished. In a third study, we explored the feasibility of using patterns of interactive pointer movements, or micro-behaviours, to detect response uncertainty. Using logistic regression and k-fold cross-validation in model training and testing, the uncertainty model achieved an estimated performance accuracy of 81%. These findings suggest that micro-behaviours provide a promising approach toward developing a better understanding of the relationship between the dynamics of pointer movements and underlying perceptual, cognitive and affective psychological mechanisms. Human-computer interaction; Pointer-tracking; Mouse movement dynamics; Decision uncertainty; On-line survey; Spatio-temporal features; Machine learningproofpublishe

Analysing online user activity to implicitly infer the mental workload of web-based tasks using defeasible reasoning

Mental workload can be considered the amount of cognitive load or effort used over time to complete a task in a complex system. Determining the limits of mental workload can assist in optimising designs and identify if user performance is affected by that design. Mental workload has also been presented as a defeasible concept, where one reason can defeat another and a 5-layer schema to represent domain knowledge to infer mental workload using defeasible reasoning has compared favourably to state-of-the-art inference techniques. Other previous work investigated using records of user activity for measuring mental workload at scale using web-based tasks For this research, a solution design and experiment were put together to analyse user activity from a web-based task to determine if mental workload can be inferred implicitly using defeasible reasoning. While there was one promising result, only weak correlation between inferred values and reference workload profile values was found