Ciblage | EPFL Graph Search

Le ciblage (en marketing) est une politique consistant à choisir des populations et produits sur lesquels concentrer l'effort de l'entreprise. Le processus de choix et d'action Dans la pratique, le ciblage est un processus comportant plusieurs étapes :

évaluer l'attrait relatif de : ** chaque segment de population où existe une demande potentielle, ** chaque type de produits et services pouvant être offerts à ces segments ** chaque canal et support de distribution de produits et services pouvant être offerts à ces segments
choisir ceux sur lesquels l'entreprise concentrera ses efforts (cible), en cohérence avec ses objectifs, ses compétences et ses ressources.
monter les opérations nécessaires pour atteindre, conquérir ou fidéliser chaque cible, et au total en tirer le meilleur parti au niveau des volumes d'affaires, de la part de marché, et de la rentabilité, dans le plan marketing

Le ciblage, en protection sociale, est un mécanisme consistant à identifier des m

Recommender Systems for Healthy Behavior Change

Onur Yürüten

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve healthy behavior change. Such a system should inspire its users with small, incremental and achievable goals. For this, it must resolve a trade-off between two opposing objectives: help the user achieve a steady improvement in target behavior, and avoid extreme goals that may injure or discourage the user. This is an unprecedented challenge in the recommender systems research. If the system understands the impacts of past interventions for behavior change, it can determine its usersâ behavioral responses to its own recommendations. This implies a specific data curation, in which we not only measure people's behavior but also deliberately introduce an intervention to monitor its effect on people's patterns. In turn, the system can use these existing users' information to derive the right procedure for effective recommendations. In this study we capitalize on this insight and develop InspiRE - our behavior recommender framework. Through InspiRE we propose the following contributions: 1) We design the data curation. 2) We develop the novel approaches for behavior profiling 3) We develop an evaluation process for this novel type of recommender system, and also compare it with traditional, similarity-based recommendation approach. We curate a dataset that contains information of daily step counts and social intervention for 83 people. InspiRE successfully uses the observations from this dataset, and proposes recommendations that are both effective and feasible. We also show that InspiRE can generalize to other dimensions of well being: we demonstrate this through a dataset that contains the snacking patterns of 73 people, who receive message-based interventions. We observe that InspiRE's recommendation strategy is in line with theories of behavior change.

EPFL2017

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Ming Jiang, Cheng Shi

Neuron detection is a key step in individualizing and counting neurons which are important for assessing physiological and pathophysiological information. A large number of methods including deep learning networks have been proposed but mainly targeting regions with few aggregated neurons. The objective of this paper is to address an automated neuron detection problem in heterogeneous hippocampus region with different degrees of neuron aggregation. Since deep learning networks require a lot of ground truths hut neuron instance annotation is impossible in regions where numerous neurons are clustered, ground truth of centroids marked at the center of neurons is created for training. We propose a multiscale convolutional neural network (CNN) to regress neuron centroid mapping across image. Using multiscale information makes the proposed network applicable not only for single individual neurons, but also for a large number of aggregated neurons. Experimental results show that our method is superior to state-of-the-art deep learning -based algorithms. To our knowledge, this is the first deep learning study to detect neurons in regions of highly clustered neurons.

IEEE2020

Secure Integration of Wireless Sensor Networks into Applications

Christophe Trefois

Wireless sensors are small devices that are able to gather, process and deliver information from a physical environment to an external system. By doing so, they open new applications in different domains, such as healthcare, traffc control, defense and agriculture. The integration of Wireless Sensor Networks (WSN) with Business Applications (BA) raises technical and security related challenges. Existing approaches target technical issues such as interoperability between WSN and BAs or heterogeneity of acquired sensor data. In this work, we start by performing an analysis of the risks that such an integration of WSNs with BAs may present using the NIST SP 800-30 recommendations. We then introduce and analyze an effcient security scheme that does not use complex operations and guarantees end-to-end confidentiality of sensor data. Finally, we provide an in silico proof-of-concept and validate it using a real WSN co-developed with Cisco Systems France.

2008

Recommender Systems for Healthy Behavior Change

Onur Yürüten

EPFL2017