Blockchain-based data privacy management with Nudge theory in open banking

Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments. © 2019 Elsevier B.V

A Rhombic Dodecahedron Topology for Human-Centric Banking Big Data

Banks are collecting an unprecedentedly large amount of data about their customers from difference sources, considering their cyber, physical, social activities. The focus of this paper is to study the problem of information sharing and lower the communication overhead among different nodes for a specific data mining approach in distributed big data architectures. This problem can be abstracted as how to efficiently search under a specific cluster node topology. This paper proposes a new design rule for topologies including: 1) low coordination number; 2) high packing density; and 3) having a 3-D structure. According to this rule, a rhombic dodecahedron topology is proposed. A distributed banking big data mining framework based on the proposed topology is implemented. The experiments based on multioptimization benchmark functions show the excellent searching ability of the proposed topology; and a banking customer feature reduction prototype has been implemented to showcase the practicality of the data mining framework

Proceedings of the tenth international conference Models in developing mathematics education: September 11 - 17, 2009, Dresden, Saxony, Germany

This volume contains the papers presented at the International Conference on “Models in Developing Mathematics Education” held from September 11-17, 2009 at The University of Applied Sciences, Dresden, Germany. The Conference was organized jointly by The University of Applied Sciences and The Mathematics Education into the 21st Century Project - a non-commercial international educational project founded in 1986. The Mathematics Education into the 21st Century Project is dedicated to the improvement of mathematics education world-wide through the publication and dissemination of innovative ideas. Many prominent mathematics educators have supported and contributed to the project, including the late Hans Freudental, Andrejs Dunkels and Hilary Shuard, as well as Bruce Meserve and Marilyn Suydam, Alan Osborne and Margaret Kasten, Mogens Niss, Tibor Nemetz, Ubi D’Ambrosio, Brian Wilson, Tatsuro Miwa, Henry Pollack, Werner Blum, Roberto Baldino, Waclaw Zawadowski, and many others throughout the world. Information on our project and its future work can be found on Our Project Home Page http://math.unipa.it/~grim/21project.htm It has been our pleasure to edit all of the papers for these Proceedings. Not all papers are about research in mathematics education, a number of them report on innovative experiences in the classroom and on new technology. We believe that “mathematics education” is fundamentally a “practicum” and in order to be “successful” all new materials, new ideas and new research must be tested and implemented in the classroom, the real “chalk face” of our discipline, and of our profession as mathematics educators. These Proceedings begin with a Plenary Paper and then the contributions of the Principal Authors in alphabetical name order. We sincerely thank all of the contributors for their time and creative effort. It is clear from the variety and quality of the papers that the conference has attracted many innovative mathematics educators from around the world. These Proceedings will therefore be useful in reviewing past work and looking ahead to the future

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms