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2022
Journal article
Unknown
HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning
Neves Oliveira B. S., Fernandes De Oliveira A., Monteiro De Lira V., Linhares Coelho Da Silva T., Fernandes De Macedo J. A.
Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report 'Anna stole Ada's car', imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.Source: Intelligent data analysis 26 (2022): 637–657. doi:10.3233/IDA-205720
DOI: 10.3233/ida-205720
Metrics:
Neves Oliveira B. S., Fernandes De Oliveira A., Monteiro De Lira V., Linhares Coelho Da Silva T., Fernandes De Macedo J. A.
Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report 'Anna stole Ada's car', imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.Source: Intelligent data analysis 26 (2022): 637–657. doi:10.3233/IDA-205720
DOI: 10.3233/ida-205720
Metrics:
See at:
Intelligent Data Analysis 
| dl.acm.org | CNR ExploRA
2022
Conference article
Open Access
Predicting vehicles parking behaviour for EV recharge optimization
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ceur-ws.org 
| ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A topological perspective of port networks from three years (2017-2019) of AIS Data
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Complex network analysis is a fundamental tool to understand non-trivial aspects of graphs and networks and is widely used in many fields. In this paper, we apply complex network techniques to study port networks, in which nodes are ports and edges are maritime lines between ports. In particular, we study the temporal evolution of several topological features of a network of ports, including connected components, shortest paths, and clustering coefficients. We built the network with three years of Automatic Identification System data from 2017 to 2019. We highlight several interesting trends and behaviors that differentiate long-range vessels from short-range vessels.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 268–275, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MASTER
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Complex network analysis is a fundamental tool to understand non-trivial aspects of graphs and networks and is widely used in many fields. In this paper, we apply complex network techniques to study port networks, in which nodes are ports and edges are maritime lines between ports. In particular, we study the temporal evolution of several topological features of a network of ports, including connected components, shortest paths, and clustering coefficients. We built the network with three years of Automatic Identification System data from 2017 to 2019. We highlight several interesting trends and behaviors that differentiate long-range vessels from short-range vessels.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 268–275, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MASTER
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2021
Report
Open Access
Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Understanding evolution of maritime networks from automatic identification system data
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a conceptual framework for the fine-grained systematic study of maritime graphs evolution. To this end, this paper presents the month-by-month analysis of world-wide graphs built using a 3-years AIS dataset. The analysis focuses on the evolution of several topological graph features, as well as their stationarity and statistical correlation. Results have revealed some interesting seasonal and trending patterns that can provide insights in the world-wide maritime context and be used as building blocks toward the prediction of graphs topology.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00451-0
DOI: 10.1007/s10707-021-00451-0
Project(s): MASTER
Metrics:
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a conceptual framework for the fine-grained systematic study of maritime graphs evolution. To this end, this paper presents the month-by-month analysis of world-wide graphs built using a 3-years AIS dataset. The analysis focuses on the evolution of several topological graph features, as well as their stationarity and statistical correlation. Results have revealed some interesting seasonal and trending patterns that can provide insights in the world-wide maritime context and be used as building blocks toward the prediction of graphs topology.Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00451-0
DOI: 10.1007/s10707-021-00451-0
Project(s): MASTER
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2021
Conference article
Restricted
Predicting the next location for trajectories from stolen vehicles
Da Silva Neto J. S., Coelho Da Silva T. L., Cruz L. A., Monteiro De Lira V., José Antônio F. De Macêdo José A. F., Magalh R. P.
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.Source: ICTAI 2021 - IEEE 33rd International Conference on Tools with Artificial Intelligence, Washington, DC, USA, 1-3/11/2021
DOI: 10.1109/ictai52525.2021.00073
Metrics:
Da Silva Neto J. S., Coelho Da Silva T. L., Cruz L. A., Monteiro De Lira V., José Antônio F. De Macêdo José A. F., Magalh R. P.
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.Source: ICTAI 2021 - IEEE 33rd International Conference on Tools with Artificial Intelligence, Washington, DC, USA, 1-3/11/2021
DOI: 10.1109/ictai52525.2021.00073
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2021
Conference article
Restricted
Transitive Halifax: an activity-based search engine for bus routes
Jinkun C., Monteiro De Lira V., Paulovich F. V., Soares A.
Transitive Halifax is an activity-oriented mobility service that allows users to search for bus routes toward places where they can perform their desired activities. The service is based on the observation that individuals often go to a place to conduct an activity. Simultaneously, the activity is often not strictly related to a single place since one may go shopping or eating in different locations. Transitive Halifax has a web interface that helps the user find the most relevant bus routes and bus stops candidates that they could use to go to places where they can perform their intended activity. The system implements a search engine that ranks the bus stops candidates according to the user's preferences and desired activities.Source: MDM 2021 - 22nd IEEE International Conference on Mobile Data Management, pp. 233–235, Toronto, ON, Canada, 15-18/06/2021
DOI: 10.1109/mdm52706.2021.00045
DOI: 10.13140/rg.2.2.34006.37445
Metrics:
Jinkun C., Monteiro De Lira V., Paulovich F. V., Soares A.
Transitive Halifax is an activity-oriented mobility service that allows users to search for bus routes toward places where they can perform their desired activities. The service is based on the observation that individuals often go to a place to conduct an activity. Simultaneously, the activity is often not strictly related to a single place since one may go shopping or eating in different locations. Transitive Halifax has a web interface that helps the user find the most relevant bus routes and bus stops candidates that they could use to go to places where they can perform their intended activity. The system implements a search engine that ranks the bus stops candidates according to the user's preferences and desired activities.Source: MDM 2021 - 22nd IEEE International Conference on Mobile Data Management, pp. 233–235, Toronto, ON, Canada, 15-18/06/2021
DOI: 10.1109/mdm52706.2021.00045
DOI: 10.13140/rg.2.2.34006.37445
Metrics:
See at:
doi.org | ResearchGate Data | ieeexplore.ieee.org | CNR ExploRA
2020
Conference article
Open Access
Uncovering vessel movement patterns from AIS data with graph evolution analysis
Carlini E., Monteiro De Lira V., Soares A., Etemad M., Brandoli Machado B., Matwin S.
The availability of the large amount of Automatic Identification System (AIS) data has fostered many studies on maritime vessel traffic during the recent years, often representing vessels and ports relationships as graphs. Although the continuous research effort, only a few works explicitly study the evolution of such graphs and often consider coarse-grained time intervals. In this context, our ultimate goal is to fill this gap by providing a systematic study in the graph evolution by considering voyages over time. three years of AIS data from the coastal waters of United States. By mining the arrivals and departures of vessels from ports, we build a graph consisting of vessel voyages between ports.We then provide a study on topological features calculated from such graphs with a strong focus on their temporal evolution. Finally, we discuss the main limitations of our approach and the future perspectives that will spawn from this work.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): MASTER
Carlini E., Monteiro De Lira V., Soares A., Etemad M., Brandoli Machado B., Matwin S.
The availability of the large amount of Automatic Identification System (AIS) data has fostered many studies on maritime vessel traffic during the recent years, often representing vessels and ports relationships as graphs. Although the continuous research effort, only a few works explicitly study the evolution of such graphs and often consider coarse-grained time intervals. In this context, our ultimate goal is to fill this gap by providing a systematic study in the graph evolution by considering voyages over time. three years of AIS data from the coastal waters of United States. By mining the arrivals and departures of vessels from ports, we build a graph consisting of vessel voyages between ports.We then provide a study on topological features calculated from such graphs with a strong focus on their temporal evolution. Finally, we discuss the main limitations of our approach and the future perspectives that will spawn from this work.Source: International Workshop in Big Mobility Data Analytics - EDBT/ICDT Workshops, Copenhagen, Denmark, 30th March - 2nd April, 2020
Project(s): MASTER
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2019
Conference article
Open Access
POLAr: Geographic placement optimization for latency sensitive applications
Monteiro De Lira V. C., Carlini E., Dazzi P.
To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.Source: The 20th IEEE International Conference on Mobile Data Management (MDM 2019), pp. 361–362, 10-13/06/2019
DOI: 10.1109/mdm.2019.00-31
Project(s): BASMATI
Metrics:
Monteiro De Lira V. C., Carlini E., Dazzi P.
To assure a timely fruition of media and interactive applications to end users is a complex challenge, especially when potentially spread worldwide, at home or in mobility. It in fact requires a careful placement of the software services on the right computational resources, such that those services are placed as close as possible to end users to mitigate the effect of network on the user experience. In this demo paper, we present a tool that aims to facilitate the placement of latency sensitive applications on computational resources, by considering the geographical positioning of the user demand, the user experience, and the budget limitation of application owners.Source: The 20th IEEE International Conference on Mobile Data Management (MDM 2019), pp. 361–362, 10-13/06/2019
DOI: 10.1109/mdm.2019.00-31
Project(s): BASMATI
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
Event attendance classification in social media
De Lira V. M., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.
Popular events are well reflected on social media, where people share their feelings and discuss their experiences. In this paper, we investigate the novel problem of exploiting the content of non-geotagged posts on social media to infer the users' attendance of large events in three temporal periods: before, during and after an event. We detail the features used to train event attendance classifiers and report on experiments conducted on data from two large music festivals in the UK, namely the VFestival and Creamfields events. Our classifiers attain very high accuracy with the highest result observed for the Creamfields festival ( similar to 91% accuracy at classifying users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Finally, we discuss an illustrative application of the methodology in the field of transportation.Source: Information processing & management 56 (2019): 687–703. doi:10.1016/j.ipm.2018.11.001
DOI: 10.1016/j.ipm.2018.11.001
Metrics:
De Lira V. M., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.
Popular events are well reflected on social media, where people share their feelings and discuss their experiences. In this paper, we investigate the novel problem of exploiting the content of non-geotagged posts on social media to infer the users' attendance of large events in three temporal periods: before, during and after an event. We detail the features used to train event attendance classifiers and report on experiments conducted on data from two large music festivals in the UK, namely the VFestival and Creamfields events. Our classifiers attain very high accuracy with the highest result observed for the Creamfields festival ( similar to 91% accuracy at classifying users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Finally, we discuss an illustrative application of the methodology in the field of transportation.Source: Information processing & management 56 (2019): 687–703. doi:10.1016/j.ipm.2018.11.001
DOI: 10.1016/j.ipm.2018.11.001
Metrics:
See at:
Information Processing & Management | ISTI Repository | Information Processing & Management | www.sciencedirect.com | CNR ExploRA
2019
Doctoral thesis
Unknown
Mining human mobility data and social media for smart ride sharing
Monteiro De Lira V.
People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. Indeed, the reduction the number of circulating vehicles is one of the most difficult challenges in large metropolitan areas. This PhD thesis proposes a research contribution with the final objective of reducing travelling vehicles. This is done towards two different directions: on the one hand, we aim to improve the efficacy of ride sharing systems, creating a larger number of ride possibilities based on the passengers destination activities; on the other hand, we propose a social media analysis method, based on machine learning, to identify transportation demand to an event.
Monteiro De Lira V.
People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. Indeed, the reduction the number of circulating vehicles is one of the most difficult challenges in large metropolitan areas. This PhD thesis proposes a research contribution with the final objective of reducing travelling vehicles. This is done towards two different directions: on the one hand, we aim to improve the efficacy of ride sharing systems, creating a larger number of ride possibilities based on the passengers destination activities; on the other hand, we propose a social media analysis method, based on machine learning, to identify transportation demand to an event.
See at: CNR ExploRA
2019
Conference article
Open Access
Mining human mobility data and social media for smart ride sharing
Monteiro De Lira V.
People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. The focus of this thesis is on improving ride sharing systems as a possible solution to reduce the number of circulating vehicles.Source: MDM 2019 - 20th IEEE International Conference on Mobile Data Management, pp. 385–386, Hong Kong, China, 10-13 June, 2019
DOI: 10.1109/mdm.2019.00-19
Metrics:
Monteiro De Lira V.
People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. The focus of this thesis is on improving ride sharing systems as a possible solution to reduce the number of circulating vehicles.Source: MDM 2019 - 20th IEEE International Conference on Mobile Data Management, pp. 385–386, Hong Kong, China, 10-13 June, 2019
DOI: 10.1109/mdm.2019.00-19
Metrics:
See at:
etd.adm.unipi.it | LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas | doi.org | ieeexplore.ieee.org | CNR ExploRA
2018
Journal article
Open Access
Boosting Ride Sharing With Alternative Destinations
De Lira V. M., Perego R., Renso C., Rinzivillo S., Times V. C.
People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can he performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the topk most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can he saved and how the ranking function can be tuned to enforce pro-environment behaviors.Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 2290–2300. doi:10.1109/TITS.2018.2836395
DOI: 10.1109/tits.2018.2836395
Project(s): MASTER
Metrics:
De Lira V. M., Perego R., Renso C., Rinzivillo S., Times V. C.
People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can he performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the topk most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can he saved and how the ranking function can be tuned to enforce pro-environment behaviors.Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 2290–2300. doi:10.1109/TITS.2018.2836395
DOI: 10.1109/tits.2018.2836395
Project(s): MASTER
Metrics:
See at:
ISTI Repository | ZENODO | IEEE Transactions on Intelligent Transportation Systems | IEEE Transactions on Intelligent Transportation Systems | ieeexplore.ieee.org | CNR ExploRA
2018
Report
Open Access
BASMATI - D2.3 Global Architecture Design
Carlini E., Coppola M., Dazzi P., De Lira V. M., Schwichtenberg A., Wacker R., Lechler C., Kim M., Lee D. altmann J., Haile N., Al-Athwari B. konstantinos Tserpes, John Violos, Vaggelis Psomakelis jamie Marshall young-Woo Jung, Dongjae Kang, Sunwook Kim, Ganis Zulfa Santoso enric Pages, Ana Juan Ferrer
The ultimate goal of the architecture work package of BASMATI is to ensure that all consortium members have a common vision of the global architecture of the system and that all developers are aware of the interfaces exported by any architecture component to others. This deliverable defines the architecture of the BASMATI platform. Overall, the objective of the BASMATI project is to design, implement and evaluate a dynamic and integrated brokerage platform targeting federated clouds that supports the dynamic needs of mobile applications and users. To this end, the BASMATI architecture provides a common ground to address key technological and research challenges in different research fields. These challenges mainly focus on three core aspects: (i) User, application and situation modelling and understanding to drive application placement; (ii) Runtime adaptivity and reconfiguration; (iii) Brokering and Offloading of application and services. The resulting architecture is divided into layers, which stem from several choices taken at design time. The first choice is the separation in the management of those services that natively run on the server side from those that run on a client device. The second choice is to separate the computational plane (how computation is organized, what are the functional dependencies from the services composing the application) from the data plane, in order to foster advanced computation and data orchestration techniques. The last, and probably the most important choice, is the definition of a specific application model (which we refer to as BEAM) that encompasses all the many views of the application within the platform. Finally, the architecture defined in this document has also been designed to support the requirements identified at the beginning of the project, from the use cases, in accordance with both the joint Korean and European use cases' needs. A final version of this document was created at M18.Source: Project report, BASMATI, Deliverable D2.3, 2018
Project(s): BASMATI
Carlini E., Coppola M., Dazzi P., De Lira V. M., Schwichtenberg A., Wacker R., Lechler C., Kim M., Lee D. altmann J., Haile N., Al-Athwari B. konstantinos Tserpes, John Violos, Vaggelis Psomakelis jamie Marshall young-Woo Jung, Dongjae Kang, Sunwook Kim, Ganis Zulfa Santoso enric Pages, Ana Juan Ferrer
The ultimate goal of the architecture work package of BASMATI is to ensure that all consortium members have a common vision of the global architecture of the system and that all developers are aware of the interfaces exported by any architecture component to others. This deliverable defines the architecture of the BASMATI platform. Overall, the objective of the BASMATI project is to design, implement and evaluate a dynamic and integrated brokerage platform targeting federated clouds that supports the dynamic needs of mobile applications and users. To this end, the BASMATI architecture provides a common ground to address key technological and research challenges in different research fields. These challenges mainly focus on three core aspects: (i) User, application and situation modelling and understanding to drive application placement; (ii) Runtime adaptivity and reconfiguration; (iii) Brokering and Offloading of application and services. The resulting architecture is divided into layers, which stem from several choices taken at design time. The first choice is the separation in the management of those services that natively run on the server side from those that run on a client device. The second choice is to separate the computational plane (how computation is organized, what are the functional dependencies from the services composing the application) from the data plane, in order to foster advanced computation and data orchestration techniques. The last, and probably the most important choice, is the definition of a specific application model (which we refer to as BEAM) that encompasses all the many views of the application within the platform. Finally, the architecture defined in this document has also been designed to support the requirements identified at the beginning of the project, from the use cases, in accordance with both the joint Korean and European use cases' needs. A final version of this document was created at M18.Source: Project report, BASMATI, Deliverable D2.3, 2018
Project(s): BASMATI
See at:
ISTI Repository | CNR ExploRA
2018
Report
Open Access
BASMATI - D3.5 Server- and Client-side Applications Adaptation and Reconfiguration: Design and Specification
Dazzi P., Carlini E., De Lira V. M., Munteanu C.
This report provides a description of the mechanisms, tools, and algorithms used to support application adaptation and reconfiguration in the BASMATI brokerage platform. At the core of this support lies the BASMATI Enriched Application Model (BEAM), which is the xml-based language in which an application is modelled and represented in BASMATI. The design principles behind the BEAM (namel: compatibility, extensibility, decomposability) are the prerequisites to provide efficient and effective geo-placement of services and applications on top of federated Cloud resources. The BEAM is made available to all the components of the platform by the Application Repository, which works as a centralization point for the BEAMs of all the applications. The decomposability of BEAM is exploited by the Decision Maker that has the task to proactively and reactively adapt the application according to the behaviour of users and resources, by means of advanced placement algorithms.Source: Project report, BASMATI, Deliverable D3.5, 2018
Project(s): BASMATI
Dazzi P., Carlini E., De Lira V. M., Munteanu C.
This report provides a description of the mechanisms, tools, and algorithms used to support application adaptation and reconfiguration in the BASMATI brokerage platform. At the core of this support lies the BASMATI Enriched Application Model (BEAM), which is the xml-based language in which an application is modelled and represented in BASMATI. The design principles behind the BEAM (namel: compatibility, extensibility, decomposability) are the prerequisites to provide efficient and effective geo-placement of services and applications on top of federated Cloud resources. The BEAM is made available to all the components of the platform by the Application Repository, which works as a centralization point for the BEAMs of all the applications. The decomposability of BEAM is exploited by the Decision Maker that has the task to proactively and reactively adapt the application according to the behaviour of users and resources, by means of advanced placement algorithms.Source: Project report, BASMATI, Deliverable D3.5, 2018
Project(s): BASMATI
See at:
ISTI Repository | CNR ExploRA
2017
Conference article
Restricted
Social Media Image Recognition for Food Trend Analysis
Amato G., Bolettieri P., Monteiro De Lira V., Muntean C. I., Perego R., Renso C.
n increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.Source: SIGIR 2017 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1333–1336, Tokyo, Japan, 7 - 11 August, 2017
DOI: 10.1145/3077136.3084142
Project(s): SoBigData
Metrics:
Amato G., Bolettieri P., Monteiro De Lira V., Muntean C. I., Perego R., Renso C.
n increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.Source: SIGIR 2017 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1333–1336, Tokyo, Japan, 7 - 11 August, 2017
DOI: 10.1145/3077136.3084142
Project(s): SoBigData
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2017
Conference article
Open Access
Exploring social media for event attendance
Monteiro De Lira V., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.
Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event.Source: ASONAM '17 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 447–450, Sydney, Australia, July 31 - August 03, 2017
DOI: 10.1145/3110025.3110080
Project(s): BASMATI, SoBigData
Metrics:
Monteiro De Lira V., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.
Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event.Source: ASONAM '17 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 447–450, Sydney, Australia, July 31 - August 03, 2017
DOI: 10.1145/3110025.3110080
Project(s): BASMATI
, SoBigData Metrics:
See at:
Enlighten | ISTI Repository | doi.acm.org | doi.org | CNR ExploRA
2017
Conference article
Restricted
User behavior and application modeling in decentralized edge cloud infrastructures
Violos J., Monteiro De Lira V., Dazzi P., Altmann J., Al-Athwari B., Schwichtenberg A., Jung Y. W., Varvarigou T., Tserpes K.
Edge computing has emerged as a solution that can accommodate complex application requirements by shifting data and computation to infrastructure elements that are more suitable to manage them given the current circumstances. The BASMATI Knowledge Extractor is a component that facilitates the modeling of the resource utilization by providing tools to analyze application usage together with user behavior. This is particularly relevant in the case of mobile applications where user context and activity are tightly coupled to the application performance.Source: GECON 2017 - 14th International Conference on the Economics of Grids, Clouds, Systems, and Services, pp. 193–203, Biarritz, France, 19-21 September, 2017
DOI: 10.1007/978-3-319-68066-8_15
Project(s): BASMATI
Metrics:
Violos J., Monteiro De Lira V., Dazzi P., Altmann J., Al-Athwari B., Schwichtenberg A., Jung Y. W., Varvarigou T., Tserpes K.
Edge computing has emerged as a solution that can accommodate complex application requirements by shifting data and computation to infrastructure elements that are more suitable to manage them given the current circumstances. The BASMATI Knowledge Extractor is a component that facilitates the modeling of the resource utilization by providing tools to analyze application usage together with user behavior. This is particularly relevant in the case of mobile applications where user context and activity are tightly coupled to the application performance.Source: GECON 2017 - 14th International Conference on the Economics of Grids, Clouds, Systems, and Services, pp. 193–203, Biarritz, France, 19-21 September, 2017
DOI: 10.1007/978-3-319-68066-8_15
Project(s): BASMATI
Metrics:
See at:
Lecture Notes in Computer Science | CNR ExploRA
2016
Report
Open Access
ProgettISTI 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
See at:
ISTI Repository | CNR ExploRA
2016
Conference article
Restricted
The ComeWithMe system for searching and ranking activity-based carpooling rides
De Lira V. M., Renso C., Perego R., Rinzivillo S., Times V. C.
COMEWITHME is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a single place, as one may go for shopping or eating to many different locations. Activity-oriented carpooling hugely increases the number of rides matching a query, thus introducing requirements on system responsiveness and ranking effectiveness that are not common to traditional carpooling services. The demoed system implements the Come-WithMe service in almost its entirety, and includes the back-end and a user-friendly mobile application for smart-phones aimed at achieving users' acceptance and usability.Source: 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 1145–1148, Pisa, Italy, 19-21 June 2016
DOI: 10.1145/2911451.2911459
Metrics:
De Lira V. M., Renso C., Perego R., Rinzivillo S., Times V. C.
COMEWITHME is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a single place, as one may go for shopping or eating to many different locations. Activity-oriented carpooling hugely increases the number of rides matching a query, thus introducing requirements on system responsiveness and ranking effectiveness that are not common to traditional carpooling services. The demoed system implements the Come-WithMe service in almost its entirety, and includes the back-end and a user-friendly mobile application for smart-phones aimed at achieving users' acceptance and usability.Source: 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 1145–1148, Pisa, Italy, 19-21 June 2016
DOI: 10.1145/2911451.2911459
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA