From process models to chatbots

The effect of digital transformation in organizations needs to go beyond automation, so that human capabilities are also augmented. A possibility in this direction is to make formal representations of processes more accessible for the actors involved. On this line, this paper presents a methodology to transform a formal process description into a conversational agent, which can guide a process actor through the required steps in a user-friendly conversation. The presented system relies on dialog systems and natural language processing and generation techniques, to automatically build a chatbot from a process model. A prototype tool – accessible online – has been developed to transform a process model in BPMN into a chatbot, defined in Artificial Intelligence Marking Language (AIML), which has been evaluated over academic and industrial professionals, showing potential into improving the gap between process understanding and execution.Peer ReviewedPostprint (author's final draft

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome

BACKGROUND AND OBJECTIVES: Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTIIPANTS AND MEASUREMENTS: Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS: In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS: Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome

Dialogue Act Taxonomy Interoperability Using a Meta-Model

International audienc

Topic switch models for dialogue management in virtual humans

This paper presents a novel data-driven Topic Switch Model based on a cognitive representation of a limited set of topics that are currently in-focus, which determines what utterances are chosen next. The transition model was statistically learned from a large set of transcribed dyadic interactions. Results show that using our proposed model results in interactions that on average last 2.17 times longer compared to the same system without our model

A model for incremental grounding in spoken dialogue systems

We present a computational model of incremental grounding, including state updates and action selection. The model is inspired by corpus-based examples of overlapping utterances of several sorts, including backchannels and completions. The model has also been partially implemented within a virtual human system that includes incremental understanding, and can be used to track grounding and provide overlapping verbal and non-verbal behaviors from a listener, before a speaker has completed her utterance