Collaborating Authors

Loughborough University researchers reveal why some of us can't multitask online

Daily Mail - Science & tech

The internet may be the most comprehensive source of information ever created but it's also the biggest distraction. Set out to find an answer on the web and it's all too easy to find yourself flitting between multiple tabs, wondering how you ended up on a page so seemingly irrelevant to the topic you started on. Past research has shown that we have a very limited capacity to perform two or more tasks at the same time and brainpower suffers when we try. But my new study suggests that some people are better at multitasking online than others. Past research has shown that we have a very limited capacity to perform two or more tasks at the same time and brainpower suffers when we try.

Men are equally capable of multi-tasking, study finds

Daily Mail - Science & tech

Women are not inherently better at multi-tasking - and that's according to scientists. A study examining the long-asserted myth has proved that men are just as capable of juggling numerous jobs simultaneously. In fact, despite years of claims to the contrary, it transpires that both genders are equally able, or unable, to do more than one task concurrently. A team of researchers led by Dr Patricia Hirsch of Germany's Aachen University reached the conclusion after analysing 48 men and 48 women, with an average age of 24, in letter or number identification tasks. Some participants were asked to pay attention to two tasks at once, known as concurrent multitasking.

Topic and Prosodic Modeling for Interruption Management in Multi-User Multitasking Communication Interactions

AAAI Conferences

When to send system-mediated interruptions within collaborative multi-human-machine environments has been widely debated in the development of interruption management systems. Unfortunately, these studies do not address when to send interruptions in multi-user, multitasking scenarios or predictors of interruptibility within communication tasks. This paper addresses the issue of predicting interruptibility within these interactions with special attention to which users are engaged in which tasks or task engagement and where users are within a current task or task structure as predictors of interruptibility. Using natural human speech from these interactions, we attempt to model task engagement and task structure to predict candidate points of interruptions. The motivation for these models and their performance in a multi-user, multitasking environment are discussed as proposals in developing communication interruption management systems. To model task structure, a task breakpoint model is proposed which performs with a 90% accuracy within a multi-user, multitasking dataset. Integrating this task breakpoint model into a real-time interaction results in an average accuracy of 93% using the proposed task breakpoint model and a rule-based model. To determine the current task in which users are engaged or task engagement, a proposed task topic model performs with an accuracy between 76-88% depending on the topic within the dataset. Closely examining task structure and task engagement as predictors of interruptibility sheds new light on a rarely explored area for system-mediated interruption timings within multi-user, multitasking communication tasks.

Evolutionary Multitasking for Semantic Web Service Composition Artificial Intelligence

Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

Swiss scientists prove women multi-task better than men

Daily Mail - Science & tech

It has long been claimed that women are better at multi-tasking than men. While some women relish the accolade, others suspect some males use it as an excuse for avoiding work. Now scientists have found strong proof that men are inferior at juggling two activities - at least compared to women under 60. Men asked to carry out complex thinking while walking on a treadmill without handrails were found to stop swinging their right arm while they walk. But women under 60 – described as'pre-menopausal' – were'surprisingly' not affected with both arms swung freely as before.