Using tangible user interfaces in computer-based training systems for low-functioning autistic children

In this paper, the design of a computer-based training (CBT) system for low-functioning autistic children is addressed. The emphasis is on ease-of-use and learning efficiency of CBT systems with different interaction styles, namely the WIMP (Window Icon Menu Pointing Device) and TUI (Tangible User Interface) interaction styles. Two WIMP-based CBT systems with different pointing devices were involved in the study. The first system applied a standard computer mouse as a pointing device, while the second one employed a touch screen instead. For the TUI-based CBT system, a tabletop setting was adopted. Based on the known characteristics of TUI and children with autism, as well as related cognitive and learning theories, the benefits of TUI for low-functioning autistic children have been investigated. Elementary skill teaching was chosen as a case study for performance evaluation of these CBT systems. Empirical results show that the touch-based and TUI-based systems offered much better ease-of-use performance than that of the mouse-based system. Regarding learning efficacy, experimental results show that the TUI-based system achieved higher skill improvement, as compared with the WIMP-based system and a non-computer training method. Some guidelines and suggestions for the design of a TUI-based system for children with autism are summarized.     

A hybrid evolutionary algorithm for the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) is an NP-hard optimization problem. RCPSP is one of the most important and challenging problems in the project management field. In the past few years, many researches have been proposed for solving the RCPSP. The objective of this problem is to schedule the activities under limited resources so that the project makespan is minimized. This paper proposes a new algorithm for solving RCPSP that combines the concepts of negative selection mechanism of the biologic immune system, simulated annealing algorithm (SA), tabu search algorithm (TS) and genetic algorithm (GA) together. The performance of the proposed algorithm is evaluated and compared to current state-of-the-art metaheuristic algorithms. In this study, the benchmark data sets used in testing the performance of the proposed algorithm are obtained from the project scheduling problem library. The performance is measured in terms of the average percentage deviation from the critical path lower bound. The experimental results show that the proposed algorithm outperforms the state-of-the-art metaheuristic algorithms on all standard benchmark data sets.