Pegah Alizadeh

@inproceedings{haikal_1767,

title = {Place Embedding across Cities in Location-based Social Networks},

author = {Christophe Haikal and Pegah Alizadeh and Christophe Rodrigues and Bi Chongke},

url = {The proceeding is not published yet:

https://www.sigapp.org/sac/sac2022/file2022/TOC-Jan-23-2022.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {ACM Symposium on Applied Computing},

address = {Brno, Czech Republic},

abstract = {In the urban computing field, analysing human mobility patterns are used to understand tourists and residents behaviours, or urban areas functionalities. Transferring the information from one city to another also assists urban strategists in comparing two cities

and applying their knowledge from one to another, such as plan- ning new touristic attractions or implementing some successful urban strategies in new cities. For this goal, in this work, we propose a twofold semi-supervised approach: we first propose a place embedding method based on users mobility trajectories in each city individually, second we learn a place translation function between two cities using the set of reviews written for each place. The translation function is based on measuring textual and semantic

similarities of the reviews. Extensive experiments on two french metropolitan areas (Lille and Bordeaux) based on the extracted data from Trip-Advisor demonstrate the effectiveness of our approach.},

keywords = {},

pubstate = {accepted},

tppubtype = {inproceedings}

}

@inproceedings{osmani_1768,

title = {Clustering Approach to Solve Hierarchical Classification Problem Complexity},

author = {Aomar Osmani and Massinissa Hamidi and Pegah Alizadeh},

url = {The proceeding is not ready yet. 

Even the list of accepted papers is not published yet. I will modify this part as soon as I have them.},

issn = {will be completed later},

year  = {2022},

date = {2022-01-01},

booktitle = {AAAI Conferece on Artificial Intelligence},

address = {Vancouver, Canada},

abstract = {In a large domain of classification problems for real applications, like human activity recognition, separable spaces between groups of concepts are easier to learn than each concept alone. This is because the search space biases required to separate groups of classes (or concepts) are more relevant than the ones needed to separate classes individually. For example, it is easier to learn the activities related to the body

movements group (running, walking) versus ?on-wheels? activities group (bicycling, driving a car), before learning more specific classes inside each of these groups. Despite the ob-

vious interest of this approach, our theoretical analysis shows a high complexity for finding an exact solution. We propose in this paper an original approach based on the association of clustering and classification approaches to overcome this limitation. We propose a better approach to learn the concepts by grouping classes recursively rather than learning them class by class. We introduce an effective greedy algorithm and two theoretical measures (namely cohesion and dispersion) to evaluate the connection between the clusters and the classes. Extensive experiments on the SHL dataset show that our approach improves classification performances while reducing the number of instances used to learn each concept.},

keywords = {},

pubstate = {accepted},

tppubtype = {inproceedings}

}

@inproceedings{zigrand_1542,

title = {Machine Learning Guided Optimization for Demand Responsive Transport Systems},

author = {Louis Zigrand and Pegah Alizadeh and Emiliano Traversi and Roberto Wolfer Calvo},

url = {https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_383.pdf},

year  = {2021},

date = {2021-06-01},

booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database},

address = {Bilbao Spain (online)},

abstract = {Most of the time, objective functions used for solving staticcombinatorial  optimization  problems  cannot  deal  efficiently  with  theirreal-time counterparts. It is notably the case of Shared Mobility Systemswhere the dispatching framework must adapt itself dynamically to thedemand. More precisely, in the context of Demand Responsive Transport(DRT) services, various objective functions have been proposed in theliterature to optimize the vehicles routes. However, these objective func-tions are limited in practice because they discard the dynamic evolutionof  the  demand.  To  overcome  such  a  limitation,  we  propose  a  MachineLearning  Guided  Optimization  methodology  to  build  a  new  objectivefunction based on simulations and historical data. This way, we are ableto take the demand's dynamic evolution into account. We also presenthow to design the main components of the proposed framework to fit aDRT application: data generation and evaluation, training process andmodel  optimization.  We  show  the  efficiency  of  our  proposed  method-ology on real-world instances, obtained in a collaboration with PadamMobility, an international company developing Shared Mobility Systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{osmani_1543,

title = {Equational Model Guided by Real-time Sensor Data to Monitor Industrial Robots},

author = {Aomar Osmani and Pegah Alizadeh and Christophe Rodrigues},

url = {https://case2021.sciencesconf.org/},

year  = {2021},

date = {2021-06-01},

booktitle = {International Conference on Automation Science and Engineering},

address = {Lyon, France},

abstract = {The monitoring of industrial robots is often en-

sured by generic simulators which model the equational aspect

of the target machines. We propose an original approach to

complete the equational simulator of a milling machine using

the accumulated data from the used sensors. This approach cre-

ates a specific simulator for each machining situation by taking

the triplet (material, cutting tool, workpiece) into account. This

improvement brings great added value to the industrial experts

and improves the efficiency of industrial robots. It allows them

to better follow and interpret the behavior of machines during

the milling process. In addition to correct the simulator using

real data, our method detects also the anomalies during the

real manufacturing performance and fixes the minor bugs

along the observed real data during its continuous simulation

mimicry. The additional interest of our model remains the

precise definition of the complementary model between the real

system and the equational simulator. This makes it possible, by

using an inductive approach to search for regularities in the

model in order to better interpret the structural differences

between the model and the system and to better understand

the situations linked to their functionalities or undesirable

situations. The intensive experiments on real data validate our

model and open up many perspectives for future works.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{osmani_1499,

title = {Hierarchical Learning of Dependent Concepts for Human Activity Recognition},

author = {Aomar Osmani and Massinissa Hamidi and Pegah Alizadeh},

editor = {Karlapalem K. et al.},

url = {https://link.springer.com/chapter/10.1007/978-3-030-75765-6_7},

issn = {978-3-030-75764-9},

year  = {2021},

date = {2021-04-01},

booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},

volume = {12713},

pages = {79-92},

publisher = {Springer, Cham},

address = {Dehli, India},

abstract = {In multi-class classification tasks, like human activity recog-

nition, it is often assumed that classes are separable. In real applications,

this assumption becomes strong and generates inconsistencies. Besides,

the most commonly used approach is to learn classes one-by-one against

the others. This computational simplification principle introduces strong

inductive biases on the learned theories. In fact, the natural connections

among some classes, and not others, deserve to be taken into account.

In this paper, we show that the organization of overlapping classes (mul-

tiple inheritances) into hierarchies considerably improves classification

performances. This is particularly true in the case of activity recognition

tasks featured in the SHL dataset. After theoretically showing the expo-

nential complexity of possible class hierarchies, we propose an approach

based on transfer affinity among the classes to determine an optimal hi-

erarchy for the learning process. Extensive experiments show improved

performances and a reduction in the number of examples needed to learn.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hamidi_1275,

title = {A Multi-View Architecture for the SHL Challenge},

author = {Massinissa Hamidi and Aomar Osmani and Pegah Alizadeh},

url = {https://dl.acm.org/doi/10.1145/3410530.3414351},

issn = {978-1-4503-8076-8},

year  = {2020},

date = {2020-09-01},

booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},

pages = {317-322},

address = {virtual event},

abstract = {To recognize locomotion and transportation modes in a user-independent manner with an unknown target phone position, we (team Eagles) propose an approach based on two main steps: reduction of the impact of regular effects that stem from each phone position, followed by the recognition of the appropriate activity. The general architecture is composed of three groups of neural networks organized in the following order. The first group allows the recognition of the source, the second group allows the normalization of data to neutralize the impact of the source on the activity learning process, and the last group allows the recognition of the activity itself. We perform extensive experiments and the preliminary results encourage us to follow this direction, including the source learning to reduce the phone position's biases and activity separately.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alizadeh_1078,

title = {Apprentissage par renforcement pour la recherche d'experts sur le web},

author = {Pegah Alizadeh and Jorge Garcia Flores and Ivan Vladimir Meza Ruiz},

url = {https://egc2020.sciencesconf.org/resource/page/id/28},

year  = {2020},

date = {2020-01-01},

booktitle = {Extraction et Gestion de Connaissances},

address = {Bruxelles, Belgique},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alizadeh_614,

title = {Calcul d'une politique de?terministe dans un MDP avec re?compenses impre?cises},

author = {Pegah Alizadeh and Aomar Osmani and Emiliano Traversi},

url = {https://egc2019.sciencesconf.org/},

year  = {2019},

date = {2019-01-01},

booktitle = {EGC 2019},

volume = {RNTI-E-35},

pages = {45-56},

address = {Metz, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}