Artificial Intelligence

痉挛性构音障碍说话者的视听孤立词录音。Subjects include 15 talkers with Cerebral Palsy and 13 age-matched healthy controls. Subjects were recruited based primarily on personal contact facilitated by disability support organizations. Subjects were selected based on self-report of either speech pathology or cerebral palsy. Before data were included in the UA-Speech distribution, the diagnosis of spastic dysarthria (sometimes mixed with other forms of dysarthria) was informally confirmed by a certified speech-language pathologist listening to these recordings.

Replication data for the fsQCA model in: "Why do companies employ prohibited unethical artificial intelligence practices?"

Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this letter presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged.

The dataset contains pressure insole data from twenty subjects who performed five tasks, comprising of two common daily activities (standing and walking), and three industry-focussed tasks (manual handling, assembly and pick and place). The speed and order in which a given task was completed was not prescribed. The data pertains to the areas of human factors, ergonomics and occupational health and safety research, among others, and enables an understanding of the force distributions involved in common tasks as well as physical and manufacturing type tasks.