Abdelkader Rhouati

Bio: Abdelkader Rhouati is an academic researcher. The author has contributed to research in topics: Public opinion & Web mining. The author has an hindex of 3, co-authored 13 publications receiving 18 citations.

Getting Model of MVVM Pattern from UML Profile

Abstract: The rejuvenation of applications to harmonize with technological watch is the major challenge for all computer boxes, frameworks and languages are constantly proliferating by offering a range of improvements in terms of security and performance, which pushes all applications to invest in order to align oneself, to orient oneself towards another perspective of application implementation has become a primacy. MVW is considered the new concept of application models where the developer can choose according to his needs, which component, for example, it can be a controller, a directive or a unit test for applications where we use the AngularJS framework, modeling an application is one of the basic steps to reach it , the emergence of new patterns press IT companies to think to renew their application architecture for more security and performance, moving from an old to a new model meets this need. AngularJS is one of the widely used frameworks for modern single-page web application development which is designed to support dynamic views in the applications. We propose an UML profile for AngularJS for building a model of an AngularJS web application, and a set of transformations that transform the model into a code template.

Sentiment Analysis of French Tweets based on Subjective Lexicon Approach: Evaluation of the use of OpenNLP and CoreNLP Tools

Abstract: Corresponding Author: Abdelkader Rhouati Team SIQL, Laboratory LSEII, ENSAO Mohammed First University, Oujda, Morocco Email: abdelkader.rhouati@gmail.com Abstract: Nowadays, sentiment analysis is becoming a very important issue of research. This paper present experimentation on sentiment analysis based on subjective lexicon method. This experimentation is tested over French tweets using \"Public Opinion Knowledge (POK)\" platform. POK is a platform consists in getting public opinion orientation from text extracted from social network and blogs, which we have developed and presented in previous papers. There are three algorithms as classifiers, which are based on Natural Language Processing Tools. The first is based on OpenNLP, the second on CoreNLP and the third on dependency analysis implemented by CoreNLP. Each classifier consists of three steps, which are Part of Speech Tagging (POS), word polarity classification and sentiment classification algorithm. On the one hand, the results are used to evaluate the use of OpenNLP and CoreNLP, on other, they draw to make a comparison between lexicon and machine-learning approaches. So, experimentation leads us to conclude that tools of sentiment analysis based on lexicon are much performant than those based on machine learning and they can reach a rate of precision of 70% and F-measure of 0.7. Also, we conclude that CoreNLP is more efficient than OpenNLP by 3% of precision, this fact is due to the efficiency of Part of Speech tagging algorithms.

Impact of robotic process automation in supply chain:: A model for task selection

Abstract: Robotic process automation (RPA) is one of the most emerging technology areas of the last decade. As the name implies, RPA is an approach to automate repetitive tasks in business operations. Many solutions are avail-able on the market by multiple vendors. Through the implementation of those RPA solutions, companies can achieve higher performance levels and lead a differentiating competitive edge. One of the first fields which have benefited from RPA is Supply Chain. This paper presents a solution for the task selection problem issue of RPA applied to the Supply Chain. A case study is also presented to demonstrate the effectiveness of the designed solution.

Toward a merged approach for cross-platform applications (web, mobile and desktop)

Abstract: Several approaches are available to create cross-platform applications. The majority of these approaches focus on purely mobile platforms. Their principle is to develop the application once and be able to deploy it to multiple mobile platforms with different operating systems (Android (Java), IOS (Objective C), Windows Phone 7 (C#), etc.). In this article, we propose a merged approach and cross-platform called ZCA "ZeroCouplage Approach". Merged to regroup the strong points of approaches: "Runtime", "Component-Based" and "Cloud-Based" thank to a design pattern which we created and named M2VC (Model-Virtual-View-Controller). Cross-platform allows creating a unique application that is deployable directly on many platforms: Web, Mobile and Desktop. In this article, we also compare our ZCA approach with others to approve its added value. Our idea, contrary to mobile approaches, consists of a given technology to implement cross-platform applications. To validate our approach, we have developed an open source framework named ZCF "ZeroCouplage Framework" for Java technology.

ZeroCouplage Framework: A Framework for Multi-supports Applications (Web, Mobile and Desktop)

Abstract: The companies are currently confronted with the implementation problem of their applications on several supports (Web, mobile and desktop). The Responsive Web Design [1, 2] partially answers to this problem as it does not allow having a mobile-native version, nor a desktop one. So, we propose a new approach which relies on the use of meta-model MDA (Model Driven Architecture) [3, 4] for the CIM and PIM models. Yet instead of having a PSM for each support, we propose a new open source framework [5] based on the adaptation of the MVC model [6] entitled as ZeroCouplage framework in order to have only one PSM, in which we will conceive and develop the same application and deploy it on several supports.

Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Abstract: Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

A Survey and Taxonomy of Core Concepts and Research Challenges in Cross-Platform Mobile Development

Abstract: Developing applications targeting mobile devices is a complex task involving numerous options, technologies, and trade-offs, mostly due to the proliferation and fragmentation of devices and platforms. As a result of this, cross-platform app development has enjoyed the attention of practitioners and academia for the previous decade. Throughout this review, we assess the academic body of knowledge and report on the state of research on the field. We do so with a particular emphasis on core concepts, including those of user experience, device features, performance, and security. Our findings illustrate that the state of research demand for empirical verification of an array of unbacked claims, and that a particular focus on qualitative user-oriented research is essential. Through our outlined taxonomy and state of research overview, we identify research gaps and challenges, and provide numerous suggestions for further research.

A Sentimental Education

Abstract: This article presents one set of results from research into professional practices in special education The research involved an analysis of what is being done in practice in the specialised area of social support and in creation of the “individual” It tried hard to construct what are the underlying principles of structuring practice and the discourse of these social workers, so as to understand the professional practice of these specialised educators But above and beyond this description of such a socio-educational grammar, the analysis reported here tries to open up a reading of the area which, starting from practice, allows us to go beyond the normal alternative means of categorising social work, so allowing the researcher to choose within the interpretation of social action between respect for the client and domination, towards seeing whether there is more autonomy or control of individuals

CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French.

Abstract: Modeling multimodal language is a core research area in natural language processing. While languages such as English have relatively large multimodal language resources, other widely spoken languages across the globe have few or no large-scale datasets in this area. This disproportionately affects native speakers of languages other than English. As a step towards building more equitable and inclusive multimodal systems, we introduce the first large-scale multimodal language dataset for Spanish, Portuguese, German and French. The proposed dataset, called CMU-MOSEAS (CMU Multimodal Opinion Sentiment, Emotions and Attributes), is the largest of its kind with 40,000 total labelled sentences. It covers a diverse set topics and speakers, and carries supervision of 20 labels including sentiment (and subjectivity), emotions, and attributes. Our evaluations on a state-of-the-art multimodal model demonstrates that CMU-MOSEAS enables further research for multilingual studies in multimodal language.

Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Abstract: Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human-Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by "the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human-machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.