Volume 33 - 2020 - Special issue CRI 2019

1. An efficient end to end verifiable voting system

Léonie Tamo Mamtio ; Gilbert Tindo.
Electronic voting systems have become a powerful technology for the improvement of democracy by reducing the cost of elections, increasing voter turnout and even allowing voters to directly check the entire electoral process. End-to-end (E2E) verifiability has been widely identified as a critical property for the adoption of such voting systems for electoral procedures. Moreover, one of the pillars of any vote, apart from the secret of the vote and the integrity of the result, lies in the transparency of the process, the possibility for the voters "to understand the underlying system" without resorting to the competences techniques. The end-to-end verifiable electronic voting systems proposed in the literature do not always guarantee it because they require additional configuration hypotheses, for example the existence of a trusted third party as a random source or the existence of a random beacon. Hence, building a reliable verifiable end-to-end voting system offering confidentiality and integrity remains an open research problem. In this work, we are presenting a new verifiable end-to-end electronic voting system requiring only the existence of a coherent voting board, fault-tolerant, which stores all election-related information and allows any party as well as voters to read and verify the entire election process. The property of our system is information guaranteed given the existence of the bulletin board, the involvement of the voters and the […]

2. Named Entity Recognition in Low-resource Languages using Cross-lingual distributional word representation

Paulin Melatagia Yonta ; Michael Franklin Mbouopda.
Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify and classify expressions such as people, location, and organization names. Many NER systems have been developed, but the annotated data needed for good performances are not available for low-resource languages, such as Cameroonian languages. In this paper we exploit the low frequency of named entities in text to define a new suitable cross-lingual distributional representation for named entity recognition. We build the first Ewondo (a Bantu low-resource language of Cameroon) named entities recognizer by projecting named entity tags from English using our word representation. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed distributional representation of words

3. Extraction of lexico-grammatic features and coupling of CRF (Conditional Random Field) units to the deep neural network for aspect extraction

Saint Germes Bienvenu Bengono Obiang ; Norbert Tsopze.
The Internet contains a wealth of information in the form of unstructured texts such as customer comments on products, events and more. By extracting and analyzing the opinions expressed in customer comments in detail, it is possible to obtain valuable opportunities and information for customers and companies. The model proposed by Jebbara and Cimiano. for the extraction of aspects, winner of the SemEval2016 competition, suffers from the absence of lexico-grammatic input characteristics and poor performance in the detection of compound aspects. We propose the model based on a recurrent neural network for the task of extracting aspects of an entity for sentiment analysis. The proposed model is an improvement of the Jebbara and Cimiano model. The modification consists in adding a CRF to take into account the dependencies between labels and we have extended the characteristics space by adding grammatical level characteristics and lexical level characteristics. Experiments on the two SemEval2016 data sets tested our approach and showed an improvement in the F-score measurement of about 3.5%.