Abstract
Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publicationauthor associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250, 000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37% accuracy in predicting matches and 91.92% in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86% of the cases where authors had no previous publication history. Copyright 2017 ACM.

Abstract

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

Abstract

Abstract
Setting: Confirmation of tuberculosis (TB) in children is difficult, so clinicians use different procedures when deciding to treat. Objective: Identify criteria to initiate and maintain TB treatment in children younger than 5 years-old, without diagnosis confirmation. Design: A web-based survey was distributed by email to the corresponding authors of journal articles on childhood TB. The observations were clustered into disjoint groups, and analyzed by Ward's method. Results: We sent out 260 questionnaires and received 64 (24.6%) responses. Forty-six respondents (71.9%) said that microbiological confirmation was not important for initiation of anti-TB treatment, and that the epidemiological context and signs/symptoms suggestive of disease were most important. Sixty-one respondents (95.3%) said that the decision to continue therapy was mainly dependent on clinical improvement. A cluster of older respondents (median age: 52 years-old) who were active at a hospital or primary health care centre placed the most value on immunological test results and chest X-rays. A cluster of younger respondents (median age: 38 years-old) who were less experienced in management of TB placed more value on Interferon Gamma Release Assay (IGRA) results and chest computed tomography (CT) scans. A cluster of respondents with more experience in treating TB and working at specialized TB centres placed greater value on the clinical results and specific radiological alterations ("tree-in-bud" pattern and pleural effusion). Conclusion: TB management varied according to the age, work location and experience of the clinicians. It is necessary to establish standardized guidelines used for the diagnosis and decision to treat TB in children. (C) 2017 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U.

Abstract
Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), a.er locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction. © 2017 ACM.