Volume 33 - Special issue CRI 2019 - 2020-21

1. Language and semantics of expressions for Grafcet model synthesis in a MDE environment

Gérard NZEBOP NDENOKA ; Maurice Tchuenté ; Emmanuel Simeu.
The GRAphe Fonctionnel de Commande Étapes Transitions (GRAFCET) is a powerful graphical modeling language for the pecification of controllers in discrete event systems.It uses expressions to express the conditions of transitions and conditional actions as well as the logical and arithmetic expressions assigned to stored actions. However, several research works has focused on the transformation of Grafcet specifications (including expressions) into control code for embedded systems. To make it easier to edit valid Grafcet models and generate code, it is necessary to propose a formalization of the Grafcet expression language permitting to validate its constructs and provide an appropriate semantics. For this, we propose a context-free grammar that generates the whole set of Grafcet expressions, by extending the usual grammars of logical and arithmetic expressions. We also propose a metamodel and an associated semantics of Grafcet expressions to facilitate the implementation of the Grafcet language. A parser of the expressions Grafcet emph G7Expr is then obtained thanks to the generator of parsers ANTLR, while the metamodel is implemented in the Eclipse EMF Model Driven Engineering (MDE) environment. The combination of the two tools makes it possible to analyze and automatically build Grafcet expressions when editing and synthesizing Grafcet models.

2. 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 political parties […]

3. Enhancing Reasoning with the Extension Rule in CDCL SAT Solvers

Rodrigue Konan Tchinda ; Clémentin Tayou Djamegni.
The extension rule first introduced by G. Tseitin is a simple but powerful rule that, when added to resolution, leads to an exponentially stronger proof system known as extended resolution (ER). Despite the outstanding theoretical results obtained with ER, its exploitation in practice to improve SAT solvers' efficiency still poses some challenging issues. There have been several attempts in the literature aiming at integrating the extension rule within CDCL SAT solvers but the results are in general not as promising as in theory. An important remark that can be made on these attempts is that most of them focus on reducing the sizes of the proofs using the extended variables introduced in the solver. We adopt in this work a different view. We see extended variables as a means to enhance reasoning in solvers and therefore to give them the ability of reasoning on various semantic aspects of variables. Experiments carried out on the 2018 and 2020 SAT competitions' benchmarks show the use of the extension rule in CDCL SAT solvers to be practically beneficial for both satisfiable and unsatisfiable instances.

4. 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

5. 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%.