Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Labor informality and market segmentation in Senegal

Understanding the selection of workers into informality is a policy priority to design programs to increase formalization across Sub-Saharan Africa, where nine out of ten workers are informal. This paper estimates a model of self-selection with entry barriers into the formal sector to identify the extent of involuntary informality in Senegal, a representative country in terms of levels of informality in West Africa and with one of the most rigid labor markets in the world. Results show that the desire of being formal is greater for workers with formal education, married and with a lower proportion of children under the age of 5 living in the household. The individual's preference for the formal sector also grows with age at a decreasing rate. Results also show that labor informality is mainly a voluntary phenomenon with 30 percent of informal workers being involuntarily displaced into the informal sector. Results are robust to different model specifications, definitions of labor informality and heterogenous groups of workers.Centro de Estudios Distributivos, Laborales y Sociale

Learning scorecard dashboards: visualizing student learning experience

This paper presents the design of dashboards for the Learning Scorecard, a platform designed for improving the student experience in a Higher Education course using gamification and Business Intelligence (BI) techniques. LS is a Learning Analytics application, that has been used in Data Warehouse and BI courses in a University setting since 2016. The LS platform has two independent views: student view and faculty (or course coordinator) view. In the LS faculty view, dashboards were designed according to the best practices of information visualization for decision support, whereas in the student view the visualization of the learning experience is immersed in gamification elements. This paper focuses only on student dashboards, which are driven by engagement and motivation of students to improve their collaboration and learning experience. A central design decision for the LS implementation, was the recognition that the way students want to track their progress and their learning experience in a course is fundamentally different that the way teachers need to monitor student progress. The presented learning dashboards use gamification mechanisms to enable the visualization of self-assessment results giving a clear indication of the learning progress of students in a course.info:eu-repo/semantics/acceptedVersio

Learning Scorecard dashboards: visualizing student learning experience

This paper presents the design of dashboards for the Learning Scorecard, a platform designed for improving the student experience in a Higher Education course using gamification and Business Intelligence (BI) techniques. LS is a Learning Analytics application, that has been used in Data Warehouse and BI courses in a University setting since 2016. The LS platform has two independent views: student view and faculty (or course coordinator) view. In the LS faculty view, dashboards were designed according to the best practices of information visualization for decision support, whereas in the student view the visualization of the learning experience is immersed in gamification elements. This paper focuses only on student dashboards, which are driven by engagement and motivation of students to improve their collaboration and learning experience. A central design decision for the LS implementation, was the recognition that the way students want to track their progress and their learning experience in a course is fundamentally different that the way teachers need to monitor student progress. The presented learning dashboards use gamification mechanisms to enable the visualization of self-assessment results giving a clear indication of the learning progress of students in a course.info:eu-repo/semantics/acceptedVersio

E-ARK Final Report

Between 2014 and 2017 the E-ARK project brought together a consortium of five European national archives, five leading research institutions, three systems providers, two government institutions, and two membership organisations to work on the development and implementation of the tools, standards, and administrative processes required to support digital archiving. The project exceeded its objectives and achieved significant results in numerous areas. In particular, it met all ten milestones; produced all 31 deliverables (plus some extra) http://www.eark-project.com/resources/project-deliverables ; was assessed as excellent in the final year review; and was dubbed a “European Showcase Project” by the Project Officer, Alina Senn, together with the two external project reviewers Adrian Brown (Parliamentary Archives, UK), and Hannes Kulovits, (Austrian National Archives)[1]. Finally, robust measures were adopted to sustain the project outputs, which are now listed by category

BCFA: Bespoke Control Flow Analysis for CFA at Scale

Many data-driven software engineering tasks such as discovering programming patterns, mining API specifications, etc., perform source code analysis over control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be expensive and performance of the analysis heavily depends on the underlying CFG traversal strategy. State-of-the-art analysis frameworks use a fixed traversal strategy. We argue that a single traversal strategy does not fit all kinds of analyses and CFGs and propose bespoke control flow analysis (BCFA). Given a control flow analysis (CFA) and a large number of CFGs, BCFA selects the most efficient traversal strategy for each CFG. BCFA extracts a set of properties of the CFA by analyzing the code of the CFA and combines it with properties of the CFG, such as branching factor and cyclicity, for selecting the optimal traversal strategy. We have implemented BCFA in Boa, and evaluated BCFA using a set of representative static analyses that mainly involve traversing CFGs and two large datasets containing 287 thousand and 162 million CFGs. Our results show that BCFA can speedup the large scale analyses by 1%-28%. Further, BCFA has low overheads; less than 0.2%, and low misprediction rate; less than 0.01%.Comment: 12 page