Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.Comment: Accepted for publication in the 25th International Conference on Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken Germany, June 24-26 202

“Bibliostory—Educational Comic Stories.” A Social Constructivist Approach to Media and Information Literacy Education for Children and Adolescents

Our paper presents a theoretical background for a Polish comic book “Bibliostory—educational comic stories” (Pl. Bibliostory—edukacyjne historie komiksowe). The comic targets children between 9 and 12 years of age and youths from 13 to 16 years of age. Each story illustrates one issue, such as information searching, organization of information, plagiarism, and information problem solving strategy. Bibliostory is based on two constructivist pedagogical concepts: the zone of proximal development (ZPD) and case-based learning/teaching. These concepts, on application level, are first of all associated with designing educational situations and relationships between teachers and students (educators and learners). The aim of our paper is to present the possibilities of application of these concepts in the educational comic books. We describe the general assumptions of two concepts, then we focus on elements applied in Bibliostory project. We also provide a review of literature on the educational potential of comic books.Zuza Wiorogórska’s work was carried out during her stay as the visiting scholar at the University of California, Berkeley, thanks to a scholarship from the Kosciuszko Foundation. Ewa Rozkosz’s work was carried out thanks to the grant awarded by the Faculty of Education of the University of Lower Silesia for the project “Społeczno-kulturowe podejście w projektowaniu materiałów edukacyjnych na potrzeby edukacji medialnej I inforamacyjnej dzieci i młodzieży na przykładzie »Bibliostory«” (nr 05/ WGW/dok/2016)

Using LCS to Exploit Order Book Data in Artificial Markets

International audienceIn the study of financial phenomena, multi-agent market order-driven simulators are tools that can effectively test different eco-nomic assumptions. Many studies have focused on the analysis of adap-tive learning agents carrying on prices. But the prices are a consequence of the matching orders. Reasoning about orders should help to anticipate future prices. While it is easy to populate these virtual worlds with agents analyzing "simple" prices shapes (rising or falling, moving averages, ...), it is nev-ertheless necessary to study the phenomena of rationality and influence between agents, which requires the use of adaptive agents that can learn from their environment. Several authors have obviously already used adaptive techniques but mainly by taking into account prices historical. But prices are only consequences of orders, thus reasoning about orders should provide a step ahead in the deductive process. In this article, we show how to leverage information from the order books such as the best limits, the bid-ask spread or waiting cash to adapt more effectively to market offerings. Like B. Arthur, we use learning classifier systems and show how to adapt them to a multi-agent system

From Human Computation to the Global Brain: the self-organization of distributed intelligence

The present chapter wishes to investigate the wider context of human computation, viewing it as merely one approach within the broad domain of distributed human-computer symbiosis. The multifarious developments in the “social ” Internet have shown the great potential of large-scale collaborativ