A New Look at Onset Transfer in Indo-European Reduplication: Dissimilation of Consonant Clusters

This is a revised version of my paper presented in a special session on reduplication at the Fall Conference of Language Research Institute, Hankuk University of Foreign Studies (Nov. 1, 2019).A new typology of onset cluster reduplication is proposed in Indo-European languages on three premises: 1) Partial reduplication in Indo-European copies the onset cluster in toto; 2) The canonical form of Grassmanns Law type of dissimilation occurs between two complex segments that are sufficiently similar; 3) Such dissimilation of complex segments typically occurs preferentially to an obstruent plus resonant (TR) cluster and to a sibilant plus obstruent (ST) cluster only as a generalization of the preferential rule. The analysis shows that, of the four logically possible rule combinations in the reduplication of TR- vs. ST-initial roots, only three actually occur in Indo-European languages. The fourth type, in which an ST cluster is reduced but a TR cluster remains, is excluded, as it violates the preferential order of dissimilation of consonant clusters. This paper also explains why Sanskrit and Old Irish reduce the ST-initial clusters differently. If the ST cluster acts as a complex segment, the more sonorant S drops, as in the Sanskrit perfect stem ta-stambh- prop, but if it acts as a consonant cluster, the less sonorant T drops, as in the Old Irish preterit stem se-scaind- spring off. This analysis offers a more coherent typology than Zukoffs (2017), which does not properly explain the acrossthe- board C2-copying, a pattern predicted to occur by his permutation of constraints, yet unattested in Indo-European languages and universally nonexistent

Continuous multibiometric authentication for online exam with machine learning

Multibiometric authentication has been received great attention over the past decades with the growing demand of a robust authentication system. Continuous authentication system verifies a user continuously once a person is login in order to prevent intruders from the impersonation. In this study, we propose a continuous multibiometric authentication system for the identification of the person during online exam using two modalities, face recognition and keystrokes. Each modality is separately processed to generate matching scores, and the fusion method is performed at the score level to improve the accuracy. The EigenFace and support vector machine (SVM) approach are applied to the facial recognition and keystrokes dynamic accordingly. The matching score calculated from each modality is combined using the classification by the decision tree with the weighted sum after the score is split into three zones of interes

Mental Workload Estimation with Electroencephalogram Signals by Combining Multi-Space Deep Models

The human brain remains continuously active, whether an individual is working or at rest. Mental activity is a daily process, and if the brain becomes excessively active, known as overload, it can adversely affect human health. Recently, advancements in early prediction of mental health conditions have emerged, aiming to prevent serious consequences and enhance the overall quality of life. Consequently, the estimation of mental status has garnered significant attention from diverse researchers due to its potential benefits. While various signals are employed to assess mental state, the electroencephalogram, containing extensive information about the brain, is widely utilized by researchers. In this paper, we categorize mental workload into three states (low, middle, and high) and estimate a continuum of mental workload levels. Our method leverages information from multiple spatial dimensions to achieve optimal results in mental estimation. For the time domain approach, we employ Temporal Convolutional Networks. In the frequency domain, we introduce a novel architecture based on combining residual blocks, termed the Multi-Dimensional Residual Block. The integration of these two domains yields significant results compared to individual estimates in each domain. Our approach achieved a 74.98% accuracy in the three-class classification, surpassing the provided data results at 69.00%. Specially, our method demonstrates efficacy in estimating continuous levels, evidenced by a corresponding Concordance Correlation Coefficient (CCC) result of 0.629. The combination of time and frequency domain analysis in our approach highlights the exciting potential to improve healthcare applications in the future.Comment: 16 pages, 5 figure