Mounim A. El Yacoubi

Institut Mines-Télécom | telecom-sudparis.eu · SAMOVAR UMR 5157: Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literat...

In recent years, finger-vein biometrics has attracted extensive attention due to its potential for accurate and efficient identification. Deep neural networks (DNNs) have proven effective in automatically extracting discriminative features from large collections of finger-vein images, resulting in improving the accuracy and efficiency of finger-vei...

The necessity of implementing new options to improve telemedicine has gained much importance in recent years. Among all the available technologies, photoplethysmography is turning out to be a promising resource. Its cost effectiveness and its usability allow its embedding in several devices without the need of constraining requirements. In this pap...

Background: Hypomimia is a symptom of Parkinson's disease (PD), characterized by a decrease in facial movements and loss of face emotional expressions. This study aims to detect hypomimia in participants with early-stage PD based on facial action units (AUs). Methods: A total of 299 video recordings were included, consisting of 208 PD subjects and...

Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can lead to blindness. Traditional approaches for retinopathy detection require expensive devices and hi...

Athlete's pose acquisition and analysis is promising to provide coaches with details of athletes performance and thus help to improve athletes' performances with more detailed supervision from coaches. Compared with traditional ways of acquiring an athlete's gesture, such as using wearable sensors, computer vision technology has advantages of low-c...

Over the last years, sensor-based continuous authentication on mobile devices has achieved great success on personal information protection. These proposed mechanisms, however, require both legal and illegal users' data for authentication model training, which takes time and is impractical. In this paper, we present MAuGANs, a lightweight and pract...

Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variations in the microcirculation. PPG technology is widely used in a variety of wearable sensors to investigate the cardiovascular system. Recent studies have demonstrated the utility of PPG analysis for carrying out large-scale screening...

Deep neural networks have been widely used in several complex tasks such as robotics, self-driving cars, medicine, etc. However, they have recently shown to be vulnerable in uncertain environments where inputs are noisy. As a consequence, the robustness of neural networks has become an essential property for their application in critical systems. R...

Vein biometrics is a high security and privacy preserving identification technology that has received increasing attentions. Although deep neural networks (DNNs), such as convolutional neural network (CNN), have been investigated for vein recognition and achieved a significant improvement in accuracy, they still fail to model long-range pixel depen...

With the enormous technological advances of recent years, the amount of digitized historical documents, both handwritten and printed, has increased. It is well known that digital historical documents are not easily processed in their original form, but they need to be transformed into a readable form in order to be automatically understood by comp...

Vein biometrics is a high security and privacy preserving identification technology that has attracted increasing attention over the last decade. Deep neural networks (DNNs), such as convolutional neural networks (CNN), have shown strong capabilities for robust feature representation, and have achieved, as a result, state-of-the-art performance on...

Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative dise...

Palm-vein identification is a highly secure pattern biometrics that has become an active research area in recent years. Despite the recent progress in deep neural networks (DNNs) for vein identification, existing solutions for feature representation continue to lack robustness due to the limited training samples. To address this limitation, data au...

Multi-view finger-vein recognition technology has attracted increasing attentions in recent years. Despite recent advances in the multi-view finger-vein identification, existing solutions employ multiple monocular cameras from different views to record two-dimensional (2D) projections of 3D vein vessels, which causes the following problems: 1) 2D i...

Palm-vein recognition has been the focus of large research efforts over the last years. However, despite the effectiveness of deep learning models, in particular Convolutional Neural Networks (CNNs), in automatically learning robust feature representations, thereby obtaining good accuracy, such good performance is usually obtained at the expense of...

(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects...

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive De...

Recently, Generative Adversarial Networks (GANs) have been widely applied for data augmentation given limited datasets. The state of the art is dominated by measures evaluating the quality of the generated images, that are typically all added to the training dataset. There is however no control of the generated data, in terms of the compromise betw...

This two-volume set constitutes the proceedings of the Third International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, which took place in Paris, France, in June 2022.The 98 full papers presented were carefully reviewed and selected from 192 submissions. The papers present new advances in the field of pattern recogni...

Detection of Alzheimer's disease from handwritten features using a photonic reservoir computer.

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside...

Artificial Neural Networks (ANNs) have amassed unprecedented success in information processing ranging from image recognition to time series prediction. The success can largely be attributed to the availability of large datasets for training and the increased complexity of the models. Unfortunately, for some applications only a limited amount of sa...

Recently, Deep Learning models, such as Deep Convolutional Neural Networks (CNNs), have shown remarkable performance on various Computer Vision tasks. Unfortunately, many application domains, such as agriculture image analysis, do not have access to large datasets. In this study, we are interested in the prediction of soil moisture dissipation rate...

Although it has received increasing researchers’ attention in recent years, palm-vein recognition still faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this chapter proposes an end-to-end genera...

Due to the sensitive nature of diabetes-related data, preventing them from being easily shared between studies, and the wide discrepancies in their data processing pipeline, progress in the field of glucose prediction is hard to assess. To address this issue, we introduce GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine learning-base...

Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variation inside the micro-circulation. PPG technology is widely used in a variety of clinical and non-clinical devices in order to investigate the cardiovascular system. One example of clinical PPG device is the pulse oxymeter, while non-c...

The adoption of deep learning in healthcare is hindered by their “black box” nature. In this paper, we explore the RETAIN architecture for the task of glucose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB a...

The standard way to train neural-network-based solutions in healthcare does not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values of people with diabetes. We propose a new training methodology that achi...

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we...

We present a vision-based activity recognition system for centrally connected humanoid robots. The robots interact with several human participants who have varying behavioral styles and inter-activity-variability. A cloud server provides and updates the recognition model in all robots. The server continuously fetches the new activity videos recorde...

Background and objectives: Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on...

Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to...

Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the forecasting of future glucose values for diabetic people. Thanks to its two-level attention mechanism, the RETAIN model is interpretable while remaining as efficient as...

Intrusion detection is an important Cyber Security task. Machine learning (ML) algorithms are often used to calssify the network traffic and detect abnormal behavior. The performance of such algorithms heavily depend on the representation of the network traffic data. Representation learning (RL) consists in learning better features representation b...

We present in this poster a theoretical formalization of the abstract operator of the Leaky ReLU function as an activation function in a deep neural network. This work is part of the verification of the robustness of neural networks by interpretation. To validate our formulation, we present our implementation of this function within the most powerf...

Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values for diabetic people. In this study, we propose th...

This report paper presents a study on detecting intrusion based on machine learning. The study was performed on the Secure Water Treatment (SWaT) testbed, a scaled-down version of a real-world industrial water treatment plant. We compare two methods : the first one using only a long short-term memory (LSTM) network adapted to time series, and the s...

The adoption of deep learning in healthcare is hindered by their "black box" nature. In this paper, we explore the RETAIN architecture for the task of glusose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB a...

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the pr...

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neur...

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starti...

Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the forecasting of future glucose values for diabetic people. Thanks to its two-level attention mechanism, the RETAIN model is interpretable while remaining as efficient as...

We present in this report a theoretical formalization of the abstract operator of the Leaky ReLU function as an activation function in a deep neural network. This work is part of the verification of the robustness of neural networks by interpretation. To validate our formulation, we present our implementation of this function within the most powerf...

Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine-learning-based glucose-predictive models. To ensure the reproducibility of the resul...

Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity throug...

Word Spotting of Historical Arabic Documents is a challenging task due to the complexity of document layouts. This paper proposes a novel word spotting approach that consists of learning feature representation to describe word images. The objective is to investigate optimal embedding spaces to extract a discriminative word image representation. The...

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error.We apply this idea to the pre...

Intrusion detection is a critical Cyber Security subject. Different Machine Learning (ML) approaches have been proposed for Intrusion Detection Systems (IDS). However, their application to real-life scenarios remains challenging due to high data dimensionality. Representation learning (RL) allows discriminative feature representation in a low dimen...

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starti...

Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. A variety of hashing methods have been developed for learning an efficient binary data representation, mainly by relaxing some imposed constraints during hash function learning. Although they have achieved good accuracy-speed trade-off, the...

Hand-vein biometrics as a high security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approach, especially vein texture segmentation, has...

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessmen...

Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on...

Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility data is collected from smartphones, mining mobile network geolocation is non-trivial as it is a sparse, coarse and noisy data for which real transport labels are unknown. In this study, we process billions of Call Detail R...

Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by...

Background: Because of the rich set of spatiotemporal features it allows to extract, online handwriting is being increasingly investigated for characterizing neurodegenerative diseases like Parkinson and Alzheimer. The state of the art on the latter is dominated by methods that extract global (average) kinematic parameters, and then apply basic cla...

Background: The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms. Methods: We extract temporal and acoustical voice features (e.g. Jitter and Harmonics-to-Noise Ratio) from read speech of patients i...

Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility data is collected from smartphones, mining mobile network geolocation is non-trivial as it is a sparse, coarse and noisy data for which real transport labels are unknown. In this study, we process billions of Call Detail R...

We present, in this paper, a novel paradigm for assessing Alzheimer's disease and aging by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile or age group, and that extract global kinema...

The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor e...

The paper proposes a novel video-based person re-identification system that consists of describing a person using both Interest Points (IP) and Image-based features. The Image-based descriptor extracts global image representation that includes the silhouette but also possibly extra objects (i.e animal, stroller, etc) while the IP-based descriptor e...

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessmen...

Introduction L’écriture est un processus cognitif qui résulte de l’interaction entre plusieurs facteurs du développement cognitifs, linguistiques, et psychomoteurs. De ce fait, la détérioration de celle-ci est un signe de dysfonctionnement dans l’une des sphères cognitives. Objectifs Notre objectif est de développer une technique innovante, permet...

Communication-enabled devices that are physically carried by individuals are today pervasive, which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata c...

Handwriting recognition is a mature technology with numerous successful applications. By contrast, handwriting analysis for health assessment is more recent and has attracted far less research efforts. In this work, we propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike...