Abstract
The central idea of the Web of Data is to interlink the information available in the Web, most of which is actually stored in databases rather than in static HTML pages. Tools to convert relational data into semantic web formats and publish then as linked data are essential to fulfill the vision of a web of data available for automatic processing, as web content is currently available to humans. This paper presents DaPress, a simple tool to publish linked data on the Web, that maps a relational database to an RDF triplestore and creates a SPARQL access point. The paper reports the use of DaPress to publish the database of Authenticus, a system that automatically assigns publication authors to known Portuguese researchers and institutions. © Teresa Costa and José Paulo Leal.

Abstract
The teaching-learning process is increasingly focused on the combination of the paradigms "learning by viewing" and "learning by doing." In this context, educational resources, either expository or evaluative, play a pivotal role. Both types of resources are interdependent and their sequencing would create a richer educational experience to the end user. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. The Seqins is a sequencing tool of digital resources that has a fairly simple sequencing model. The tool communicates through the IMS LTI specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning. © Ricardo Queirós, José Paulo Leal and José Campos.

Abstract
Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

Abstract
The ability to have an automated real time detection of user interest during a web session is very appealing and can be very useful for a number of web intelligence applications. Low level interaction events associated with user interest manifestations form the basis of user interest models. However such data sets present a number of challenges from a machine learning perspective, including the level of noise in the data and class imbalance (given that the majority of content will not be of interest to a user). In this paper we evaluate a large number of machine learning techniques aimed at learning from class imbalanced data using two data sets collected from a real user study. We use the AUC, recall, precision and model complexity to compare the relative merits of these techniques and conclude that useful models with AUC above 0.8 can be obtained using a mix of sampling and cost based methods. Ensemble models can provide further accuracy but make deployment more complex.

Abstract
XSLT is a powerful and widely used language for transforming XML documents. However, its power and complexity can be overwhelming for novice or infrequent users, many of whom simply give up on using this language. On the other hand, many XSLT programs of practical use are simple enough to be automatically inferred from examples of source and target documents. An inferred XSLT program is seldom adequate for production usage but can be used as a skeleton of the final program, or at least as scaffolding in the process of coding it. It should be noted that the authors do not claim that XSLT programs, in general, can be inferred from examples. The aim of Vishnu-the XSLT generator engine described in this chapter-is to produce XSLT programs for processing documents similar to the given examples and with enough readability to be easily understood by a programmer not familiar with the language. The architecture of Vishnu is composed by a graphical editor and a programming engine. In this chapter, the authors focus on the editor as a GWT Web application where the programmer loads and edits document examples and pairs their content using graphical primitives. The programming engine receives the data collected by the editor and produces an XSLT program. Copyright (C) 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Abstract
Automated assessment is an essential part of eLearning. Although comparatively easy for multiple choice questions (MCQs), automated assessment is more challenging when exercises involve languages used in computer science. In this particular case, the assessment is more than just grading and must include feedback that leads to the improvement of the students’ performance. This paper presents ongoing work to develop Kora, an automated diagram assessment tool with enhanced feedback, targeted to the multiple diagrammatic languages used in computer science. Kora builds on the experience gained with previous research, namely: a diagram assessment tool to compute di erences between graphs; an IDE inspired web learning environment for computer science languages; and an extensible web diagram editor. Kora has several features to enhance feedback: it distinguishes syntactic and semantic errors, providing specialized feedback in each case; it provides progressive feedback disclosure, controlling the quality and quantity shown to each student after a submission; when possible, it integrates feedback within the diagram editor showing actual nodes and edges on the editor itself. © Hélder Correia, José Paulo Leal, and José Carlos Paiva