Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

Development of the scalable readout system for micro-pattern gas detectors and other applications

Abstract: Developed within RD51 Collaboration for the Development of Micro-Pattern Gas Detectors Technologies, the Scalable Readout System (SRS) is intended as a general purpose multi-channel readout solution for a wide range of detector types and detector complexities. The scalable architecture, achieved using multi-Gbps point-to-point links with no buses involved, allows the user to tailor the system size to his needs. The modular topology enables the integration of different front-end ASICs, giving the user the possibility to use the most appropriate front-end for his purpose or to build a heterogeneous experimental apparatus which integrates different front-ends into the same DAQ system. Current applications include LHC upgrade activities, geophysics or homeland security applications as well as detector R&D. The system architecture, development and running experience will be presented, together with future prospects, ATCA implementation options and application possibilities.

Detecting positive and negative deceptive opinions using PU-learning

Abstract: Detection of negative deceptive opinion spam.Improved PU-learning approach.Compares the performance of the proposed approach and the original PU-learning method.The role of opinions' polarity in the detection of deception.Reports experimental results on a set of negative deceptive opinions. Nowadays a large number of opinion reviews are posted on the Web. Such reviews are a very important source of information for customers and companies. The former rely more than ever on online reviews to make their purchase decisions, and the latter to respond promptly to their clients' expectations. Unfortunately, due to the business that is behind, there is an increasing number of deceptive opinions, that is, fictitious opinions that have been deliberately written to sound authentic, in order to deceive the consumers promoting a low quality product (positive deceptive opinions) or criticizing a potentially good quality one (negative deceptive opinions). In this paper we focus on the detection of both types of deceptive opinions, positive and negative. Due to the scarcity of examples of deceptive opinions, we propose to approach the problem of the detection of deceptive opinions employing PU-learning. PU-learning is a semi-supervised technique for building a binary classifier on the basis of positive (i.e., deceptive opinions) and unlabeled examples only. Concretely, we propose a novel method that with respect to its original version is much more conservative at the moment of selecting the negative examples (i.e., not deceptive opinions) from the unlabeled ones. The obtained results show that the proposed PU-learning method consistently outperformed the original PU-learning approach. In particular, results show an average improvement of 8.2% and 1.6% over the original approach in the detection of positive and negative deceptive opinions respectively.