Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning

Electrochemical removal of bromate from drinking water

The electrochemical removal of bromate on a tin cathode has been studied by both electrochemical techniques, such as cyclic voltammetry and chronoamperometry, as well as by steady-state electrolytic experiments. The reduction of bromate in 2M NaCl takes place efficiently at potentials more negative than -1.4V vs. Ag/AgCl and the rate of the reduction displays a maximum at about -1.8 V, then decreases and consequently it increases again as the potential becomes more negative than -1.9 V. The % removal efficiency of bromate displays a maximum (75.6%) at -1.8 V, while the % selectivity of bromide displays a minimum (70.3%) at the same potential