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The interview documents early days in the field of disaster risk reduction.

The transcript and video were developed in the context of a United Nations Office for Disaster Risk Reduction (UNDRR) project on the History of DRR.

The transcript presents important developments during the 1980s with valuable lessons about risk reduction.

It takes the readers on a history of the journey of DRR over three decades.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The purpose of this paper is to conduct research with children, who have different abilities from adults, in terms of language understanding and level of attention, is a challenging task, especially concerning novel interactive systems such as social robots. Consequently, self-reporting methods are often replaced or supplemented by observational methods that are usually carried out taking advantage of video recordings. However, some limitations make this approach challenging for studies conducted with groups of children in real-world environments, whose relevance is being addressed more and more frequently in human-robot interaction (HRI) research. Thus, there is a growing need for rigorous observation approaches in unstructured test environments.

This paper presents an alternative analysis approach, in relation to an experimental child-robot interaction (CRI) application, which was developed at the Academy of Arts and Design, Tsinghua University, China. The proposed methodology is based on the analysis of video recordings of in-wild activities of children with a robot. The methodology has the aim of providing a framework to facilitate knowledge identification and structuring. It was implemented for experiment evaluation and validation purposes and to propose a reference structure for the organization of new experiments and the stimulation of new ideas and activities in the design process.

This methodology provides a logical structure, which can be used to identify the effectiveness or limits of design choices, pertaining to such aspects as the morphology or movement of robots or the choice of their specific role in education, all of which play crucial roles in the design process and could be improved to achieve better results. This structured identification is a practical implication for the design process, above all when it is oriented toward social robots and their interaction with children or elderly senile people. In this case, the outcomes were the identification of important elements of an experiment (psychological profiles of the involved children and possible problems or risks) and their impact on the design process.

The methodological approach, which structures and uses cognitive maps to elaborate multicriteria evaluation models, is not new to the operations research field (where it is defined as a multimethodology application of Soft OR), but it has not yet been applied in the field of HRI studies, to analyze children’s perception of a robot and to identify the factors that can affect a good CRI or to structure knowledge that can be shared to guide the design process of robots for the experience of children playing.

Progress accomplished by the disabled entrepreneurs on the fronts of profits, turnover, return on investment (ROI), employees engaged, capital employed and diversification shall be studied and prevalence of gender differences in such progress shall be assessed.

The proposed research is descriptive in nature, based on primary data, collected by personally administering a well-structured interview schedule to 201 disabled entrepreneurs in Puducherry selected using a snowball sampling technique. Data collected has been analyzed using SPSS 21, using the tools of mean, one-way ANOVA, factorial ANOVA and chi-square (χ²) analysis.

The prevalence rate of entrepreneurship among female disabled is very low. Female disabled entrepreneurs manage higher turnover than their male counterparts and manage insignificantly higher progress in terms of capital employed, while male disabled entrepreneurs have managed insignificantly higher progress in terms of profits, diversification and ROI. Illiterate disabled, both men and women, struggle to manage decent turnover while the better educated manage better turnover.

This paper has highlighted the low prevalence rate of entrepreneurship among women disabled though the fewer women disabled entrepreneurs are performing better than their male counterparts in operating their business.

The findings of this paper may be taken as base for formulation of effective government policies in empowering disabled persons in general and women disabled in particular.