26 research outputs found
Enquête sur l'enquête 'Les réseaux économiques souterrains en cité de transit (1981-2010)' de Jean-François Laé et Numa Murard
L’enquête « Les réseaux économiques souterrains en cité de transit » a été réalisée par Jean-François Laé, professeur émérite de l’Université Paris 8 Vincennes – Saint Denis et Numa Murard, professeur émérite de l’Université Paris Diderot. Elle a la particularité d’avoir été menée en deux fois, puisqu’elle a donné lieu à une première enquête réalisée au début des années 1980 puis à un retour sur enquête en 2010. L’origine de cette recherche remonte à l’expérience de Jean-François Laé comme travailleur social dans une cité dite de transit de la ville d’Elbeuf, en Seine-Maritime. Après sa rencontre avec Numa Murard au CERFI (Centre d’études, de recherche et de formation institutionnelle), ils décident tous deux de réaliser cette enquête, ayant obtenu des financements de la CNAF (Caisse nationale des affaires familiales) et du ministère de l’Urbanisme et du Logement. Elle donnera lieu à la rédaction d’un rapport et à la publication d’un ouvrage en 1985, L’Argent des pauvres. Trente ans plus tard, les deux chercheurs décident de revenir sur les terrains de leur première enquête, dans le cadre d’un documentaire radiophonique. Un ouvrage sera publié suite à ce retour, intitulé Deux générations dans la débine et paru en 2012. Pour l’enquête initiale comme pour le retour sur enquête, les deux chercheurs se sont immergés en ethnographes dans la vie quotidienne des habitants de la cité de transit. S’ils se sont principalement focalisés sur la vie économique des enquêtés, ils ont ouvert un ensemble de thématique allant bien au-delà de ce que laisse à penser le titre de l’enquête. Si la méthodologie est particulière, la méthode d’exposition l’est tout autant puisqu’elle ressort de ce que Jean-François Laé et Numa Murard appellent la « sociologie narrative ». Le corpus de documents fourni par les chercheurs a trait aux deux étapes de cette recherche. Il réunit notamment un carnet de terrain et le rapport publié suite à la première enquête, de même que différentes notes préparatoires, des photos et des transcriptions d’enregistrements collectés lors du retour sur enquête. S’il est parcellaire du fait de la perte de certains documents, ce corpus donne une idée précise des méthodes d’enquête des deux chercheurs et ouvre des pistes de réutilisation, notamment dans un cadre pédagogique.
Deux entretiens ont été réalisés par l'équipe beQuali avec les auteurs de l'enquête : le premier avec Jean-François Laé,Numa Murard et Fabien Deshayes au CRESPPA, le deuxième avec Jean-François Laé et Numa Murard au CDSP
Multiple Instance Learning using Bag Distribution Parameters
In pattern recognition and data analysis, objects or events are often represented by a feature vector with a fixed length. For some applications this is a severe limitation, and extensions have been proposed. One approach is Multiple-Instance Learning (MIL). Here, objects are represented by a collection of feature vectors (called a bag) and a bag is labeled positive, when at least one feature vector is member of a concept. In some situations it is not suitable to assume the presence of a concept, and the distribution of all the feature vectors in a bag is required to classify the bag. In this paper we propose a simple bag classification scheme using the parameters of the fitted distributions. Experiments show sometimes surprisingly good performances with respect to other state-of-the-art approaches.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Semi-supervised rail defect detection from imbalanced image data
Rail defect detection by video cameras has recently gained much attention in bothacademia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning techniques. In this paper we investigate if positive defective candidates selected from the unlabeled data can help improve the balance between the two classes and gain performance on detecting a specic type of defects called Squats. We compare data sampling techniques as well and conclude that the semi-supervised techniques are a reasonable alternative for improving performance on applications such as rail track Squat detection from image data.Railway EngineeringPattern Recognition and Bioinformatic
Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised learning is often limited by the amount of available labeled data, which is typically expensive to obtain. To address this problem, we propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. The performance of the proposed SSL framework is compared with two fully supervised baselines. The results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial, and a simple classifier trained on SSL-learned representations outperforms fully supervised deep neural networks. However, the results reveal that the SSL-learned representations are too focused on encoding the subjects. Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce. The high inter-subject variability suggests that there is still room for improvements in learning representations. In general, the results suggest that SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes. Pattern Recognition and Bioinformatic
Detecting outliers from pairwise proximities: Proximity isolation forests
Because outliers are very different from the rest of the data, it is natural to represent outliers by their distances to other objects. Furthermore, there are many scenarios in which only pairwise distances are known, and feature-based outlier detection methods cannot directly be applied. Considering these observations, and given the success of Isolation Forests for (feature-based) outlier detection, we propose Proximity Isolation Forest, a proximity-based extension. The methodology only requires a set of pairwise distances to work, making it suitable for different types of data. Analogously to Isolation Forest, outliers are detected via their early isolation in the trees; to encode the isolation we design nine training strategies, both random and optimized. We thoroughly evaluate the proposed approach on fifteen datasets, successfully assessing its robustness and suitability for the task; additionally we compare favourably to alternative proximity-based methods.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic
Improving temporal interpolation of head and body pose using Gaussian process regression in a matrix completion setting
This paper presents a model for head and body pose estimation (HBPE) when labelled samples are highly sparse. The current state-of-the-art multimodal approach to HBPE utilizes the matrix completion method in a transductive setting to predict pose labels for unobserved samples. Based on this approach, the proposed method tackles HBPE when manually annotated ground truth labels are temporally sparse. We posit that the current state of the art approach oversimplifies the temporal sparsity assumption by using Laplacian smoothing. Our final solution uses: i) Gaussian process regression in place of Laplacian smoothing, ii) head and body coupling, and iii) nuclear norm minimization in the matrix completion setting. The model is applied to the challenging SALSA dataset for benchmark against the state-of-the-art method. Our presented formulation outperforms the state-of-the-art significantly in this particular setting, e.g. at 5% ground truth labels as training data, head pose accuracy and body pose accuracy is approximately 62% and 70%, respectively. As well as fitting a more flexible model to missing labels in time, we posit that our approach also loosens the head and body coupling constraint, allowing for a more expressive model of the head and body pose typically seen during conversational interaction in groups. This provides a new baseline to improve upon for future integration of multimodal sensor data for the purpose of HBPE.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic
Multimodal Joint Head Orientation Estimation in Interacting Groups via Proxemics and Interaction Dynamics
Human head orientation estimation has been of interest because head orientation serves as a cue to directed social attention. Most existing approaches rely on visual and high-fidelity sensor inputs and deep learning strategies that do not consider the social context of unstructured and crowded mingling scenarios. We show that alternative inputs, like speaking status, body location, orientation, and acceleration contribute towards head orientation estimation. These are especially useful in crowded and in-the-wild settings where visual features are either uninformative due to occlusions or prohibitive to acquire due to physical space limitations and concerns of ecological validity. We argue that head orientation estimation in such social settings needs to account for the physically evolving interaction space formed by all the individuals in the group. To this end, we propose an LSTM-based head orientation estimation method that combines the hidden representations of the group members. Our framework jointly predicts head orientations of all group members and is applicable to groups of different sizes. We explain the contribution of different modalities to model performance in head orientation estimation. The proposed model outperforms baseline methods that do not explicitly consider the group context, and generalizes to an unseen dataset from a different social event.Pattern Recognition and Bioinformatic
Conversation Group Detection With Spatio-Temporal Context
In this work, we propose an approach for detecting conversation groups in social scenarios like cocktail parties and networking events, from overhead camera recordings. We posit the detection of conversation groups as a learning problem that could benefit from leveraging the spatial context of the surroundings, and the inherent temporal context in interpersonal dynamics which is reflected in the temporal dynamics in human behavior signals, an aspect that has not been addressed in recent prior works. This motivates our approach which consists of a dynamic LSTM-based deep learning model that predicts continuous pairwise affinity values indicating how likely two people are in the same conversation group. These affinity values are also continuous in time, since relationships and group membership do not occur instantaneously, even though the ground truths of group membership are binary. Using the predicted affinity values, we apply a graph clustering method based on Dominant Set extraction to identify the conversation groups. We benchmark the proposed method against established methods on multiple social interaction datasets. Our results showed that the proposed method improves group detection performance in data that has more temporal granularity in conversation group labels. Additionally, we provide an analysis in the predicted affinity values in relation to the conversation group detection. Finally, we demonstrate the usability of the predicted affinity values in a forecasting framework to predict group membership for a given forecast horizon. Pattern Recognition and Bioinformatic