Isabelle/PIDE as Platform for Educational Tools

The Isabelle/PIDE platform addresses the question whether proof assistants of the LCF family are suitable as technological basis for educational tools. The traditionally strong logical foundations of systems like HOL, Coq, or Isabelle have so far been counter-balanced by somewhat inaccessible interaction via the TTY (or minor variations like the well-known Proof General / Emacs interface). Thus the fundamental question of math education tools with fully-formal background theories has often been answered negatively due to accidental weaknesses of existing proof engines. The idea of "PIDE" (which means "Prover IDE") is to integrate existing provers like Isabelle into a larger environment, that facilitates access by end-users and other tools. We use Scala to expose the proof engine in ML to the JVM world, where many user-interfaces, editor frameworks, and educational tools already exist. This shall ultimately lead to combined mathematical assistants, where the logical engine is in the background, without obstructing the view on applications of formal methods, formalized mathematics, and math education in particular.Comment: In Proceedings THedu'11, arXiv:1202.453

A rule-based functional-structural model of rice considering source and sink functions

As a first step towards a generic genotype-phenotype model of rice, we present here a model of the growth and morphology of rice in combination with ecophysiological processes using the technique of functional-structural plant modelling (FSPM) and the interactive modelling platform GroIMP along with the graph-based Relational Growth Grammar formalism. The model constitutes a simple yet functionally coherent phenotype model of rice, consisting of a set of morphogenetic RGG rules describing an “average” developmental course and final morphology, partially linking yield traits to processes (tiller and grain number, stem length, grain filling rate, grain weight)

Group Communication Patterns for High Performance Computing in Scala

We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable/Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 % efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000

Scala Server Faces

Progress in the Java language has been slow over the last few years. Scala is emerging as one of the probable successors for Java with features such as type inference, higher order functions, closure support and sequence comprehensions. This allows object-oriented yet concise code to be written using Scala. While Java based MVC frameworks are still prevalent, Scala based frameworks along with Ruby on Rails, Django and PHP are emerging as competitors. Scala has a web framework called Lift which has made an attempt to borrow the advantages of other frameworks while keeping code concise. Since Sun’s MVC framework, Java Server Faces 2.0 and its future versions seem to be heading in a reasonably progressive direction; I have developed a framework which attempts to overcome its limitations. I call such a framework ―Scala Server Faces‖. This framework provides a way of writing Java EE applications in Scala yet borrow from the concept of ―convention over configuration‖ followed by rival web frameworks. Again, an Eclipse tool is provided to make the programmer\u27s task of writing code on the popular Eclipse platform. Scala Server Faces, the framework and the tool allows the programmer to write enterprise web applications in Scala by providing features such as templating support, CRUD screen generation for database model objects, an Ant script to help deployment and integration with the Glassfish Application Server

Privacy-Preserving Genetic Relatedness Test

An increasing number of individuals are turning to Direct-To-Consumer (DTC) genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies like 23andme and Ancestry.com have started to offer popular and affordable ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private genetic tests. In this paper, we present an effective protocol for privacy-preserving genetic relatedness test (PPGRT), enabling a cloud server to run relatedness tests on input an encrypted genetic database and a test facility's encrypted genetic sample. We reduce the test to a data matching problem and perform it, privately, using searchable encryption. Finally, a performance evaluation of hamming distance based PP-GRT attests to the practicality of our proposals.Comment: A preliminary version of this paper appears in the Proceedings of the 3rd International Workshop on Genome Privacy and Security (GenoPri'16