CRACS

Permanent URI for this community

http://localhost:4000/handle/123456789/28
This service develops its activity in the areas of programming languages, parallel and distributed computing, data mining, intelligent systems and software architecture, with emphasis on solving concrete problems in areas of multidisciplinary collaboration, such as Biology, Medicine and Chemistry.

Browse

Browsing CRACS by Title

  • Item
  • Item
  • Item
  • Item
    6th Symposium on Languages, Applications and Technologies, SLATE 2017, June 26-27, 2017, Vila do Conde, Portugal
    ( 2017) Ricardo Queirós ; Pinto,M ; Simões,A ; José Paulo Leal ; Varanda Pereira,MJ
  • Item
    Accelerating Recommender Systems using GPUs
    ( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra
    We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).
  • Item
    Accelerating Recommender Systems using GPUs
    ( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra
  • Item
    Access Control and Obligations in the Category-Based Metamodel: A Rewrite-Based Semantics
    ( 2015) Sandra Alves ; Degtyarev,A ; Fernandez,M
    We define an extension of the category-based access control (CBAC) metamodel to accommodate a general notion of obligation. Since most of the well-known access control models are instances of the CBAC metamodel, we obtain a framework for the study of the interaction between authorisation and obligation, such that properties may be proven of the metamodel that apply to all instances of it. In particular, the extended CBAC metamodel allows security administrators to check whether a policy combining authorisations and obligations is consistent.
  • Item
    Active Manifold Learning with Twitter Big Data
    ( 2015) Silva,C ; Mário João Antunes ; Costa,J ; Ribeiro,B
    The data produced by Internet applications have increased substantially. Big data is a flaring field that deals with this deluge of data by using storage techniques, dedicated infrastructures and development frameworks for the parallelization of defined tasks and its consequent reduction. These solutions however fall short in online and highly data demanding scenarios, since users expect swift feedback. Reduction techniques are efficiently used in big data online applications to improve classification problems. Reduction in big data usually falls in one of two main methods: (i) reduce the dimensionality by pruning or reformulating the feature set; (ii) reduce the sample size by choosing the most relevant examples. Both approaches have benefits, not only of time consumed to build a model, but eventually also performance-wise, usually by reducing overfitting and improving generalization capabilities. In this paper we investigate reduction techniques that tackle both dimensionality and size of big data. We propose a framework that combines a manifold learning approach to reduce dimensionality and an active learning SVM-based strategy to reduce the size of labeled sample. Results on Twitter data show the potential of the proposed active manifold learning approach.
  • Item
    Adaptive learning for dynamic environments: A comparative approach
    ( 2017) Costa,J ; Silva,C ; Mário João Antunes ; Ribeiro,B
    Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).
  • Item
    Adaptive model rules from data streams
    ( 2013) Ezilda Duarte Almeida ; Carlos Ferreira ; João Gama
    Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms. © 2013 Springer-Verlag.
  • Item
    An adjustable sensor platform using dual wavelength measurements for optical colorimetric sensitive films
    ( 2014) Carlos Manuel Machado ; Gouveia,C ; João Ferreira ; Kovacs,B ; Pedro Jorge ; Luís Lopes
    We present a new and versatile sensor platform to readout the response of sensitive colorimetric films. The platform is fully self-contained and based on a switched dual-wavelength scheme. After filtering and signal processing, the system is able to provide self-referenced measures of color intensity changes in the film, while being immune to noise sources such as ambient light and fluctuations in the power source and in the optical path. By controlling the power and the switching frequency between the two wavelengths it is possible to fine tune the output gain as well as the operational range of the sensor for a particular application, thus improving the signal conditioning. The platform uses a micro-controller that complements the analog circuit used to acquire the signal. The latter pre-amplifies, filters and conditions the signal, leaving the micro-controller free to perform sensor linearization and unit conversion. By changing the sensitive film and the wavelength of the light source it is possible to use this platform for a wide range of sensing applications. © 2014 IEEE.
  • Item
    ANALYSING RELEVANT INTERACTIONS BY BRIDGING FACEBOOK AND MOODLE
    ( 2016) Luciana Gomes Oliveira ; Álvaro Figueira
  • Item
    Analyzing Social Media Discourse An Approach using Semi-supervised Learning
    ( 2016) Álvaro Figueira ; Luciana Gomes Oliveira
    The ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics' applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an "editorial model" that characterizes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.
  • Item
    An Approach to Relevancy Detection: contributions to the automatic detection of relevance in social networks
    ( 2016) Álvaro Figueira ; Miguel Oliveira Sandim ; Paula Teixeira Fortuna
    In this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.
  • Item
    An architecture for seamless configuration, deployment, and management of wireless sensor-actuator networks
    ( 2014) Edgard Santos Neto ; Mendes,R ; Luís Lopes
    The goal of this work is to provide (non-specialist) users with the means to seamlessly setup and monitor a Wireless Sensor-Actuator Network (WSN) without writing any code or performing subtle hardware configurations. Towards this goal, we present an architecture that allows the seamless configuration, deployment and management of applications over WSN. We explore the fact that most deployments have a common modus operandi: (a) simple data readers running on the nodes periodically gather and send data to sinks, and; (b) sinks process incoming data and, accordingly, issue actuation commands to the nodes. We argue that, given the knowledge of a platform's capabilities, its sensors and actuators and their respective programming interfaces, it is possible to fully automate the process of configuring, building, and deploying an application over a WSN. Similarly, monitoring and managing the deployment can be vastly simplified by using a middleware that supports user defined tasks that process data from the nodes, divide the WSN into regions, defined by simple boolean predicates over data, and eventually issue actuation commands on regions.
  • Item
    An architecture for the rapid development of XML-based web applications
    ( 2009) José Paulo Leal ; 5125
    Our research goal is the generation of working web applications from high level specifications. Based on our experience in using XML transformations for that purpose, we applied this approach to the rapid development of database management applications. The result is an architecture that defines of a web application as a set of XML transformations, and generates these transformations using second order transformations from a database schema. We used the Model-View-Controller architectural pattern to assign different roles to transformations, and defined a pipeline of transformations to process an HTTP request. The definition of these transformations is based on a correspondence between data-oriented XML Schema definitions and the Entity-Relationship model. Using this correspondence we were able produce transformations that implement database operations, forms interfaces generators and application controllers, as well as the second order transformations that produce all of them. This paper includes also a description of a RAD system following this architecture that allowed us to perform a critical evaluation of this proposal.
  • Item
    Asura: A Game-Based Assessment Environment for Mooshak (Short Paper)
    ( 2018) José Paulo Leal ; José Carlos Paiva ; 5125 ; 6251
    Learning to program is hard. Students need to remain motivated to keep practicing and to overcome their difficulties. Several approaches have been proposed to foster students’ motivation. As most people enjoy playing games of some kind and play on a regular basis, the use of games is one of the most widely spread approaches. However, taking full advantage of games to teach specific concepts of programming requires much effort. This paper presents Asura, a game-based assessment environment built on top of Mooshak that challenges students to code Software Agents (SAs) to play a game, allowing them to test the SAs against each others’ SAs and watch a movie of the test. Once the challenge development stage ends, teachers are able to organize game-like tournaments among SAs. One of the key features of Asura is that it provides a means to reduce the required effort of building game-based challenges up to that of creating traditional programming exercises. © José Carlos Paiva and José Paulo Leal.
  • Item
    Authoring Game-Based Programming Challenges to Improve Students’ Motivation
    ( 2020) José Paulo Leal ; José Carlos Paiva ; Ricardo Queirós ; 5125 ; 6251 ; 5695
    One of the great challenges in programming education is to keep students motivated while working on their programming assignments. Of the techniques proposed in the literature to engage students, gamification is arguably the most widely spread and effective method. Nevertheless, gamification is not a panacea and can be harmful to students. Challenges comprising intrinsic motivators of games, such as graphical feedback and game-thinking, are more prone to have longterm positive effects on students, but those are typically complex to create or adapt to slightly distinct contexts. This paper presents Asura, a game-based programming assessment environment providing means to minimize the hurdle of building game challenges. These challenges invite the student to code a Software Agent to solve a certain problem, in a way that can defeat every opponent. Moreover, the experiment conducted to assess the difficulty of authoring Asura challenges is described. © 2020, Springer Nature Switzerland AG.
  • Item
    Automated Assessment in Computer Science Education: A State-of-the-Art Review
    ( 2022) Álvaro Figueira ; José Paulo Leal ; José Carlos Paiva ; 5088 ; 5125 ; 6251
    Practical programming competencies are critical to the success in computer science (CS) education and goto-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.
  • Item
    Automated Assessment in Computer Science Education: A State-of-the-Art Review
    ( 2022) Álvaro Figueira ; José Paulo Leal ; José Carlos Paiva ; 5088 ; 5125 ; 6251
    Practical programming competencies are critical to the success in computer science (CS) education and goto-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.