Detecting Inspiring Content on Social Media

Inspiration moves a person to see new possibilities and transforms the way they perceive their own potential. Inspiration has received little attention in psychology, and has not been researched before in the NLP community. To the best of our knowledge, this work is the first to study inspiration through machine learning methods. We aim to automatically detect inspiring content from social media data. To this end, we analyze social media posts to tease out what makes a post inspiring and what topics are inspiring. We release a dataset of 5,800 inspiring and 5,800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.Comment: accepted at ACII 202

Characterization of Multiple Groups of Data

In this paper we propose a new approach for computing characterizations of sets of data by means of partially defined Boolean functions. The main objective is to provide minimal sets of characters that allows the user to discriminate groups of Boolean data representing individuals described by means of presence or absence of characters. Compared to previous approaches, our algorithms are more efficient and are able to compute complete sets of solutions, which may be useful according to our underlying application domain in plant biology

Experimental Approach for Bacterial Strains Characterization

In plant biology, data acquisition is no longer necessarily a major problem but nevertheless the treatment and the use of these data are still difficult. In this work, we are particularly interested by the characterization of strains of phytopathogenic bacterias, which is an important issue in the study of plant diseases. We study and compare several methods computing the smallest possible characterizations. These experiments have allowed us to characterize specific strains and diagnosis tests have been produced and used

The bacterial strains characterization problem

The accurate characterization of collections of bacterial strains is a major scientific challenge, since bacteria are indeed responsible of significant plant diseases and thus subjected to official control procedures (e.g., in Europe, Directive 2000/29/EC). The development of diagnostic tests is therefore an important issue in order to routinely identify strains of these species

SafetyKit: first aid for measuring safety in open-domain conversational systems

The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing a taxonomy of three observed phenomena: the Instigator, Yea-Sayer, and Impostor effects. We then empirically assess the extent to which current tools can measure these effects and current systems display them. We release these tools as part of a “first aid kit” (SafetyKit) to quickly assess apparent safety concerns. Our results show that, while current tools are able to provide an estimate of the relative safety of systems in various settings, they still have several shortcomings. We suggest several future directions and discuss ethical considerations

Logical characterization of groups of data: a comparative study

This paper presents an approach for characterizing groups of data represented by Boolean vectors. The purpose is to find minimal set of attributes that allow to distinguish data from different groups. In this work, we precisely defined the multiple characterization problem and the algorithms that can be used to solve its different variants. Our data characterization approach can be related to Logical Analysis of Data and we propose thus a comparison between these two methodologies. The purpose of this paper is also to precisely study the properties of the solutions that are computed with regards to the topological properties of the instances. Experiments are thus conducted on real biological data

Accelerated algorithm for computation of all prime patterns in logical analysis of data

The analysis of groups of binary data can be achieved by logical based approaches. These approaches identify subsets of relevant Boolean variables to characterize observations and may help the user to better understand their properties. In logical analysis of data, given two groups of data, patterns of Boolean values are used to discriminate observations in these groups. In this work, our purpose is to highlight that different techniques may be used to compute these patterns. We present a new approach to compute prime patterns that do not provide redundant information. Experiments are conducted on real biological data

Zoonosis emergence linked to agricultural intensification and environmental change

A systematic review was conducted by a multidisciplinary team to analyze qualitatively best available scientific evidence on the effect of agricultural intensification and environmental changes on the risk of zoonoses for which there are epidemiological interactions between wildlife and livestock. The study found several examples in which agricultural intensification and/or environmental change were associated with an increased risk of zoonotic disease emergence, driven by the impact of an expanding human population and changing human behavior on the environment. We conclude that the rate of future zoonotic disease emergence or reemergence will be closely linked to the evolution of the agriculture–environment nexus. However, available research inadequately addresses the complexity and interrelatedness of environmental, biological, economic, and social dimensions of zoonotic pathogen emergence, which significantly limits our ability to predict, prevent, and respond to zoonotic disease emergence