Emotion Recognition in Human-Computer Interaction

The authors are concerned with the development of computer systems that are capable of using information from faces and voices to recognise people's emotions in real-life situations. The paper addresses the nature of the challenges that lie ahead, and provides an assessment of the progress that has been made in the areas of signal processing and analysis techniques (with regard to speech and face), and the psychological and linguistic analyses of emotion. Ongoing developmental work by the authors in each of these areas is described.

Hardware Performance Analysis of the SHACAL-2 Encryption Algorithm

A hardware performance analysis of the SHACAL-2 encryption algorithm is presented in this paper. SHACAL-2 was one of four symmetric key algorithms chosen in the New European Schemes for Signatures, Integrity and Encryption (NESSIE) initiative in 2003. The paper describes a fully pipelined encryption SHACAL-2 architecture implemented on a Xilinx Field Programmable Gate Array (FPGA) device that achieves a throughput of over 25 Gbps. This is the fastest private key encryption algorithm architecture currently available. The SHACAL-2 decryption algorithm is also defined in the paper as it was not provided in the NESSIE submission.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Computer usage and the validity of self-assessed computer competence among first-year business students

Abstract This study evaluates the reliability of self-assessment as a measure of computer competence. This evaluation is carried out in response to recent research which has employed self-reported ratings as the sole indicator of students’ computer competence. To evaluate the reliability of self-assessed computer competence, the scores achieved by students in self-assessed computer competence tests are compared with scores achieved in objective tests. The results reveal a statistically significantly over-estimation of computer competence among the students surveyed. Furthermore, reported pre-university computer experience in terms of home and school use and formal IT education does not affect this result. The findings call into question the validity of using self-assessment as a measure of computer competence. More generally, the study also provides an up-to-date picture of self-reported computer usage and IT experience among pre-university students from New Zealand and South-east Asia and contrasts these findings with those from previous research.