Abstract
The methods used to produce news rankings by recommender systems are not public and it is unclear if they reflect the real importance assigned by readers. We address the task of trying to forecast the number of times a news item will be tweeted, as a proxy for the importance assigned by its readers. We focus on methods for accurately forecasting which news will have a high number of tweets as these are the key for accurate recommendations. This type of news is rare and this creates difficulties to standard prediction methods. Recent research has shown that most models will fail on tasks where the goal is accuracy on a small sub-set of rare values of the target variable. In order to overcome this, resampling approaches with several methods for handling imbalanced regression tasks were tested in our domain. This paper describes and discusses the results of these experimental comparisons.

Abstract
Assessment plays a central role in any educational process as a way of evaluating the students' knowledge on the concepts associated with learning objectives. The assessment of free-text answers is a process that, besides being very costly in terms of time spent by teachers, may lead to inequities due to the difficulty in applying the same evaluation criteria to all answers. This paper describes a system composed by several modules whose main goal is to work as a formative assessment tool for students and to help teachers creating and assessing exams as well monitoring students' progress. The system automatically creates training exams for students to practice based on questions from previous exams and assists teachers in the creation of evaluation exams with various kinds of information about students' performance. The system automatically assesses training exams to give automatic feedback to students. The correction of free-text answers is based on the syntactic and semantic similarity between the student answers and various reference answers, thus going beyond the simple lexical matching. For this, several pre-processing tasks are performed in order to reduce each answer to its more manageable canonical form. Besides the syntactic and semantic similarity between answers, the way the teacher evaluates the answers is also acquired. To accomplish that, the assessment is done using sub scores defined by the teacher concerning parts of the answer or its subgoals. The system has been trained and tested on exams manually graded by History teachers. There is a good correlation between the evaluation of the instructors and the evaluation performed by our system.

Abstract
Interactive visualization of virtual environments is an active research topic. There is a multiplicity of applications such as simulation systems, augmented and mixed reality environments, computer games, amongst others, which endlessly demand for greater levels of realism and interaction. At every stage of the process, including modeling, image synthesis, transmission and navigation, there are identifiable circumstances which may compromise the achievement of high quality solutions for the posed problems. For many of these problems, an effective use of optimization tools can play a major role in order to achieve solutions with better quality. Within this context, an innovative optimization architecture is presented regarding to two major principles. The first principle comprises the possibility to integrate, with reduced effort, the optimization tools with existent applications and systems. Thus, we propose an agent-based framework where the optimization application may operate as an independent process in respect to the visualization application where communication is achieved by means of a specifically developed high-level message based protocol. The second principle establishes on the utilization of a class of intelligent optimization methods, known as metaheuristics, which major distinguishing quality is their great level of problem-independence, thus, enabling a wider application. The paper describes conducted experiments and presents results that demonstrate the utility and efficacy of the proposed framework.

Abstract
The growing demand for virtual urban models in emerging areas of interest has promoted a variety of techniques for automating the creation of virtual scenes with a high level of realism. However, visualization is not always the only aim in many applications. Increasingly, urban systems are also required for management, analysis, or interaction with very different types of information. Three-dimensional (3D) urban entities should maintain neighbor relations, and being inserted in a geospatial database in order to enable associative queries, one of the most important features associated with 3D urban geographic information system (GIS). Additional challenges are found when these three-dimensional models are implemented in web-based systems. This paper proposes a method for automatic modeling of buildings and street surfaces of entire real cities by combining computer graphics, computational geometry, and GIS techniques. The sources of information employed are two-dimensional (2D) GIS and its associated digital elevation models (DEMs) in order to provide real terrain features. The virtual environment supports web-based navigation and data interaction. The case study was performed in Jaen, Spain, a hilly city with more than 1,000 blocks of buildings. The 3D city model was generated using the public data provided by the general cadastral office of Jaen and the DEM information of this area. The topological relations between the geometric urban entities were inserted in the spatial database and the graphics and thematic information could be accessed through the Internet. All these features are highly desirable for decision making in urban planning. DOI: 10.1061/(ASCE)UP.1943-5444.0000131. (C) 2013 American Society of Civil Engineers.

Abstract

Abstract
Introduction. The strong dynamic nature of the Web is a well-known reality. Nonetheless, research on Web dynamics is still a minor part of mainstream Web research. This is largely the case in Web link analysis. In this paper we investigate and measure the impact of time in link-based ranking algorithms on a particular subset of the Web, specifically blogs. Method. Using a large collection of blog posts that span more than three years, we compare a traditional link-based ranking algorithm with a time-biased alternative, providing some insights into the evolution of link data over time. We designed two experiments to evaluate the use of temporal features in authority estimation algorithms. In the first experiment we compare time-independent and time-sensitive ranking algorithms with a reference rank based on the total number of visits to each blog. In the second, we use feedback from communication media domain experts to contrast different rankings of Portuguese news Websites. Results. The distribution of citations to a Web document over time contains valuable information. Based on several examples we show that time-independent algorithms are unable to capture the correct popularity of sites with high citation activity. Using a reference rank based on the number of visits to a site, we show that a time-biased approach has a better performance. Conclusions. Although both time-independent and time-aware approaches are based on the same raw data, the experiments indicate that they can be treated as complementary signals for relevance assessment by information retrieval systems. We show that temporal information present in blogs can be used to derive stable time-dependent features, which can be successfully used in the context of Web document ranking.