Mohammed Al Achhab

Short: Graphical Specification and Automatic Verification of Business Process

This paper deals with the integration of the formal verification techniques of business process (BP) in the design phase. In order to achieve this purpose, we use the graphical notation of Business Process Modeling Notation (BPMN) for modeling BP and specifying constraint properties to be verified. A formal semantics for some response properties are given.

Syntactic entropy for main content extraction from web pages

In this paper, we present a solution for main content identification in web pages. Our solution is language-independent; Web pages may be written in different languages. It is topic-independent; no domain knowledge or dictionary is applied. And it is unsupervised; no training phase is necessary. The solution exploits the tree structure of web pages and the frequencies of text tokens to attribute scores of content density to the areas of the page and by the way identify the most important one. We tested this solution over representative examples of web pages to show how efficient and accurate it is. The results were satisfying.

A technical reading in statistical and neural machines translation (SMT & NMT)

Automatic translation of natural languages has been an active body of research in the last decades, especially when it comes to statistical translation which uses machine learning algorithms for translation tasks. Machine translation being a key application in the field of natural language processing, it leads to develop many approaches namely, statistical machine translation and recently neural machine translation. In this paper, we present a survey of the state of the art, where we describe the context of the current research studies by reviewing both the statistical machine translation and neural machine translation, and an overview of the main strengths and limitations of the two approaches.

Using the CBR Dynamic Method to Correct the Generates Learning Path in the Adaptive Learning System

The adaptive learning systems have the capacity to adapt the learning process to the needs/the rhythms of each learner, the learning styles and the preferences, but they do not ensure an individualized follow-up in real time. In this article, we will present our architecture of an Adaptive Learning System using Dynamic Case-Based Reasoning. This architecture is based on the learning styles of Felder-Silverman and the Bayesian Network to propose the learning path according to the adaptive style and on the other hand on the approach of the Dynamic Case-Based Reasoning to ensure a prediction of the dynamic situation during the learning process, when the learner has difficulty learning. This approach is based on the reuse of past similar experiences of learning (learning path) by analyzing learners’ traces.

ALS_CORR[LP]: An adaptive learning system based on the learning styles of Felder-Silverman and a Bayesian network

The aim of this paper is to present the adaptive learning system called ALS_CORR[LP]1. This system belongs to a very specific class of the e-learning systems, which is the adaptive learning ones. In fact they have the ability to adapt the learning process according to each learner needs, learning styles, objectives, etc. ALS_CORR[LP] is based on the learner prerequisites and the learning styles of Felder-Silverman, to design the learner model. As for the domain model, it is designed according to the recommendations of the differentiated pedagogy, which advocates creating multiple versions of the same learning object. Finally in order to ensure the adaptation inside the system, a Bayesian network, to match the designed learning object with the specifics of the learner profile was developed. It is also necessary to emphasize, that the major feature of the system is, its ability to correct the generated learning path in case of a failure in the evaluation phase. The learning path relevance is questioned, based on a recommendation system which enables updating the initial profile, or recommending the most relevant versions of the learning object, in case where the similarity calculation in behavior, reveals that the observed behavior in the system does not fit the initial profile description.

A probabilistic method for business process verification: Reachability, Liveness and deadlock detection

In this paper we deal with business process verification for reachability, liveness and deadlock-freeness properties. Actual business process verification techniques use formal methods such as model checking that checks exhaustively temporal logics properties, however, in this case, the business process model must be mapped to an abstract mathematical model (like Petri nets, timed automata or process algebra) on which the verification process could be done. The fact that the verification is done on this second representation can fake the results if the transformation is either erroneous or over-constrained as well as if the proprieties are misspecified. In this article we illustrate that probability calculation can be used in business process verification by evaluating a disjunctive normal form of its conditions, moreover, the verification process could be done directly on the business process model and can handle all its functional elements. We have developed an application that numerically shows the performance of our proposed technique by verifying several workflows with various “known” flaws.

Recommender system for ubiquitous learning based on decision tree

In recent years, the fast development of mobile, wireless communication and sensor technologies has provided new possibilities for supporting learning activities. Ubiquitous learning, which is learning that can take place anywhere and anytime, is the best example. In order to provide learners with adequate learning experience, factors such learners characteristics and context should be considered. Managing the learner context can help delivering the best resource adaptation services. Learning object proposed to the learner is obtained from contexual informations using the decision tree model. On the present paper, a recommender system for ubiquitous learning using context information of the learner and a decision tree model is presented, and k-fold cross validation is used in the experiment for estimating and validating the performance of our recommender system for ubiquitous learning.

Towards a Formal Semantics and Analysis of BPMN Gateways

This paper deals with formal verification of BPMN models. The lack of an unambiguous definition of the BPMN notations, and the mixing of incompatible BPMN patterns may lead to wrong or incomplete semantics, resulting in some behavioral errors such as deadlocks and multiple termination problems. As formal verification requires formal specification models and in order to create a correct business process, most used approaches consider the formalization of either a subclass of BPMN patterns or specific forms of these patterns. In this paper, thanks to Max+ Algebra, we propose to extend existing approaches by including most of BPMN notations.

Towards formal verification of business process using a graphical specification

Bringing a high confidence to the validity of business processes is one of the prevailing themes in business process management. This paper deals with the integration of the formal verification techniques of business process (BP) in the design phase. The main objective of this paper is to propose a user-friendly graphical interface that business experts can use to specify the right dynamic behaviors of BP. In order to achieve this purpose, we use the graphical notation of BPMN (Business process modeling notation) for modeling BP. The graphical interface proposed uses the same notations as those used in BPMN. In this way, the designer can automatically and intuitively validate constraint specifications on designed processes at this early stage. Several formal semantics for these properties are given and a verification approach is then proposed.