Real-time Arabic scene text detection using fully convolutional neural networks

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter is more effective compared to the standard ACS

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB)

LSTM based models stability in the context of Sentiment Analysis for social media

Deep learning techniques have proven their effectiveness for Sentiment Analysis (SA) related tasks. Recurrent neural networks (RNN), especially Long Short-Term Memory (LSTM) and Bidirectional LSTM, have become a reference for building accurate predictive models. However, the models complexity and the number of hyperparameters to configure raises several questions related to their stability. In this paper, we present various LSTM models and their key parameters, and we perform experiments to test the stability of these models in the context of Sentiment Analysis.Comment: Short note, 3 pages, MoroccoAI Annual Conference 202

Sentiment analysis in SemEval: a review of sentiment identification approaches

ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions

Detecting and Shadows in the HSV Color Space using Dynamic Thresholds

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds

Detecting and Shadows in the HSV Color Space Using Dynamic Thresholds

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds

Using Sentiment Analysis to Explore Student Feedback: A Lexical Approach

Given the increasing abundance of online courses over the last couple of years, new forms of student feedback, which are less frequently used by teachers, have been generated in massive amounts. Nonetheless, extracting and processing this student generated content manually is costly and time consuming. In this respect, our objective in this paper is to propose a lexical-based approach that can predict the underlying sentiments of each student review, thus, enabling teachers to assess to what extent are students satisfied with the online learning resources and teaching practices. To enhance the performance of the proposed approach, a new education sentiment lexicon was built and incorporated into the model. After its implementation on a dataset that was extracted from the Web, this sentiment analysis lexical approach has proven to correctly predict the sentiment polarities of the great majority (i.e. 86.45%) of student feedback

Moroccan higher education students’ and teachers’ perceptions towards using Web 2.0 technologies in language learning and teaching

The objective of this paper is to examine Moroccan higher education students’ and teachers’ perceptions and attitudes towards using Web 2.0 technologies in language learning and teaching. The results of the study revealed that all the informants were immersed in using these Internet-based applications for personal and educational purposes. Nevertheless, while language learners reported to make beneficial uses of these online platforms as language learning tools, the great majority of the interviewed faculty members did not really benefit from these platforms. Although language teachers acknowledged that Web 2.0 technologies had a positive impact on language teaching and learning, most of them were still reluctant to incorporate these tools in educational practice. The findings demonstrated that most teachers’ use of these applications was limited to sending or transferring web links and learning materials produced by other Internet users. Rather than making effective use of Web 2.0 technologies and applications as teaching facilities, most teachers used them only as a means of communication

Academic writing MOOCs – a blended learning approach

The paper discusses a blended learning approach to teaching academic writing using an externally created MOOC fully incorporated into the existing pedagogic design. The authors intend to demonstrate potentials and limitations of mixed model learning in the ESP classroom. To this day there has been little research of repurposing MOOCs for language classes, as they are usually more practical and interactive. However, the authors believe that the online component of the blended course creates an additional dimension for language acquisition and allows to address numerous general issues on academic writing which are not traditionally discussed in ESP/EAP classes. Based on the results of the case study conducted at RUDN university, the study outlines numerous benefits of blended learning trajectory. However, the investigation revealed a few challenges, some of which can be easily remedied, whereas others are of more problematic nature