CRACS - Indexed Articles in Conferences

Permanent URI for this collection

http://localhost:4000/handle/123456789/124

Browse

Recent Submissions

Now showing 1 - 5 of 162
  • Item
    FOSTERING EFFICIENT LEARNING IN THE TECHNICAL FIELD OF ROBOTICS BY CHANGING THE AUTONOMOUS DRIVING COMPETITION OF THE PORTUGUESE ROBOTICS OPEN
    ( 2017) Costa,V ; João Soares Resende ; Sousa,A ; Reis,L ; Patrícia Raquel Sousa ; Lau,N ; 6866 ; 6761
  • Item
    Streaming sensor data from dynamically reprogrammable tasks running on mobile devices
    ( 2017) Silva,N ; Eduardo Brandão Marques ; Luís Lopes
  • Item
    Sophistication as randomness deficiency
    ( 2013) Mota,F ; Aaronson,S ; Luís Filipe Antunes ; Souto,A
    The sophistication of a string measures how much structural information it contains. We introduce naive sophistication, a variant of sophistication based on randomness deficiency. Naive sophistication measures the minimum number of bits needed to specify a set in which the string is a typical element. Thanks to Vereshchagin and Vitányi, we know that sophistication and naive sophistication are equivalent up to low order terms. We use this to relate sophistication to lossy compression, and to derive an alternative formulation for busy beaver computational depth. © 2013 Springer-Verlag.
  • Item
    POPSTAR at RepLab 2013: Name ambiguity resolution on Twitter
    ( 2013) Saleiro,P ; Rei,L ; Pasquali,A ; Carlos Manuel Soares ; Teixeira,J ; Pinto,F ; Mohammad Nozari ; Catarina Félix Oliveira ; Strecht,P
    Filtering tweets relevant to a given entity is an important task for online reputation management systems. This contributes to a reliable analysis of opinions and trends regarding a given entity. In this paper we describe our participation at the Filtering Task of RepLab 2013. The goal of the competition is to classify a tweet as relevant or not relevant to a given entity. To address this task we studied a large set of features that can be generated to describe the relationship between an entity and a tweet. We explored different learning algorithms as well as, different types of features: text, keyword similarity scores between enti-ties metadata and tweets, Freebase entity graph and Wikipedia. The test set of the competition comprises more than 90000 tweets of 61 entities of four distinct categories: automotive, banking, universities and music. Results show that our approach is able to achieve a Reliability of 0.72 and a Sensitivity of 0.45 on the test set, corresponding to an F-measure of 0.48 and an Accuracy of 0.908.
  • Item
    Numerical limits for data gathering in wireless networks
    ( 2013) Mohammad Nozari ; Aguiar,A
    In our previous work, we proposed to use a vehicle network for data gathering, i.e. as an urban sensor. In this paper, we aim at understanding the theoretical limits of data gathering in a time slotted wireless network in terms of maximum service rate per node and end to end packet delivery ratio. The capacity of wireless networks has been widely studied and boundaries for that capacity expressed in Bachmann-Landau notation [1]. But these asymptotic limits do not clarify the numeric limits on data packets that can be carried by a wireless network. In this paper, we calculate the maximum data that each node can generate before saturating the network. The expected number of collision and its effect of the PDR% and service rate are investigated. The results quantify the trade off between packet delivery rate and service rate. Finally, we verify our analytical results by simulating the same scenario. © 2013 IEEE.