myTea: Connecting the Web to Digital Science on the Desktop

Bioinformaticians regularly access the hundreds of databases and tools that are available to them on the Web. None of these tools communicate with each other, causing the scientist to copy results manually from a Web site into a spreadsheet or word processor. myGrids' Taverna has made it possible to create templates (workflows) that automatically run searches using these databases and tools, cutting down what previously took days of work into hours, and enabling the automated capture of experimental details. What is still missing in the capture process, however, is the details of work done on that material once it moves from the Web to the desktop: if a scientist runs a process on some data, there is nothing to record why that action was taken; it is likewise not easy to publish a record of this process back to the community on the Web. In this paper, we present a novel interaction framework, built on Semantic Web technologies, and grounded in usability design practice, in particular the Making Tea method. Through this work, we introduce a new model of practice designed specifically to (1) support the scientists' interactions with data from the Web to the desktop, (2) provide automatic annotation of process to capture what has previously been lost and (3) associate provenance services automatically with that data in order to enable meaningful interrogation of the process and controlled sharing of the results

Domain Generalization by Solving Jigsaw Puzzles

Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the task of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images. This secondary task helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task. Multiple experiments on the PACS, VLCS, Office-Home and digits datasets confirm our intuition and show that this simple method outperforms previous domain generalization and adaptation solutions. An ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral

JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

From Spaghetti Bowl to Jigsaw Puzzle? Addressing the Disarray in the World Trade System

The rise of mega-regionals such as the Regional Comprehensive Economic Partnership (RCEP) and the Trans-Pacific Partnership (TPP) suggests that the world trade system is fragmenting to the point it appears more like a jigsaw puzzle than a spaghetti bowl. There are both regional and global jigsaw puzzles to be solved—in that order—to clean up the world trade system. But is this even likely? The difficulties of free trade agreement (FTA) consolidation at the regional level are well known, while piecing together the blocs around the world to form a coherent whole is even more challenging. In this context, a way forward is to return to the most widely used modality of trade liberalization—unilateral actions—but this time involving the multilateralization of preferences rather than unreciprocated reductions in tariff rates. As more and more FTAs are negotiated, preference erosion sets in, reducing the resistance of FTA partners to multilateralization. Multilateralization of preferences may then present a practical way forward in addressing the disarray in the world trade system

Epik Platform - Design and Development of Interactive Puzzles as an Educational Activity

Nowadays, many types of games are used as an educational tool, capturing more easily students attention by keeping them interested in the lectured topics, which accelerates the learning process and provides collaborative learning in an entertaining way. Puzzle games in specific are one of the most used games for educational purposes due to its variety of formats, themes, and logics to solve them, having an adjustable difficulty according to students capacity of problem solving, with the view of developing these capacities. Despite this gain of popularity, there are not a lot of options regarding computacional platforms to develop educational games. The Epik Platform consists in a web-based framework dedicated to the management of didactic contents and development of educational games. With this platform, the creation and edition of games turned to the educational environment is quite simplified, which makes it a good option for this purpose [1]. However, this platform’s games only include quiz type activities of multiple choices, true or false and matching, not offering much variety for the users to work with. Having this in mind, the development of interactive puzzle activities of different types, which is the main objective of this thesis, will allow the extension of the range of activities available in the set of games at the Epik platform. To be able to develop the interactive puzzle activities, a study must be done so that the right framework is selected according to the needs and limitations of development. With the addition of this new type of activity, the users will be able to develop their own interactive puzzles according to the three different puzzle games included, each one having a specific development environment for it. Overall, the platform will now be able to provide games performing a group of activities of different kinds in the same game, making it more appealing for the users. At a global level, this dissertation is inserted on the project ’Restructure, Flexibilize and Update the Epik Platform’, which aims to reimplement, extend and restructure features of the Epik platform, whose development was made for a previous master thesis by a student (Bruno Sampaio) [2]