The Experts below are selected from a list of 1767 Experts worldwide ranked by ideXlab platform

Vaglini Gigliola - One of the best experts on this subject based on the ideXlab platform.

  • Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Alfeo, Antonio Luca, Lepri Bruno, Pentland, Alexander Sandy, Cimino, Mario Gca, Vaglini Gigliola
    Abstract:

    © 2019 IEEE. In Turkey, the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrian refugees, by analyzing a real-world call detail record (CDR) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension

  • Assessing refugees' integration via spatio-temporal similarities of mobility and calling behaviors
    2019
    Co-Authors: Luca Alfeo Antonio, Pentland Alex, Lepri Bruno, Cimino Mario, Vaglini Gigliola
    Abstract:

    In Turkey, the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrian refugees, by analyzing a real-world call detail record (CDR) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension

  • Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy
    2019
    Co-Authors: Alfeo, Antonio Luca, Pentland Alex, Lepri Bruno, Cimino Mario, Vaglini Gigliola
    Abstract:

    The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational Stigmergy. By using computational Stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational Stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only i n certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015

  • Detecting permanent and intermittent purchase hotspots via computational Stigmergy
    'Scitepress', 2019
    Co-Authors: Alfeo Antonio, Lepri Bruno, Cimino Mario, Pentland, Alexander Sandy, Vaglini Gigliola
    Abstract:

    Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational Stigmergy. By using computational Stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational Stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015

  • Stigmergy-based modeling to discover urban activity patterns from positioning data
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Alfeo, Antonio L., Pentland Alex, Lepri Bruno, Cimino, Mario G. C. A., Egidi Sara, Vaglini Gigliola
    Abstract:

    Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on Stigmergy. By using Stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015

Gigliola Vaglini - One of the best experts on this subject based on the ideXlab platform.

  • design and simulation of the emergent behavior of small drones swarming for distributed target localization
    Journal of Computational Science, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Nicoletta De Francesco, Massimiliano Lega, Gigliola Vaglini
    Abstract:

    Abstract A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically inspired processes: Stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios.

  • Fostering distributed business logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination
    Information Systems Frontiers, 2018
    Co-Authors: Francesco P. Appio, Alessandro Lazzeri, Andrea Lazzeri, Antonella Martini, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assembling business facts together while protecting data source. We propose a privacy-preserving perturbation technique which is based on digital Stigmergy. Stigmergy is a processing schema based on the principle of self-aggregation of marks produced by data. Stigmergy allows protecting data privacy, because only marks are involved in aggregation, in place of actual data values, without explicit data modeling. This paper discusses the proposed approach and examines its characteristics through actual scenarios.

  • Sleep behavior assessment via smartwatch and stigmergic receptive fields
    Personal and Ubiquitous Computing, 2018
    Co-Authors: Antonio L. Alfeo, Filippo Palumbo, Davide La Rosa, Paolo Barsocchi, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.

  • A Stigmergy-Based Analysis of City Hotspots to Discover Trends and Anomalies in Urban Transportation Usage
    IEEE Transactions on Intelligent Transportation Systems, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Sara Egidi, Bruno Lepri, Gigliola Vaglini
    Abstract:

    A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on Stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing Stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e. hotspots), analyze their activity over time, and unfold anomalies. Furthermore, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

  • Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions
    'MDPI AG', 2018
    Co-Authors: Mario G C A Cimino, Alessandro Lazzeri, Witold Pedrycz, Gigliola Vaglini
    Abstract:

    In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based Stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard’s similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity

Antonio L. Alfeo - One of the best experts on this subject based on the ideXlab platform.

  • design and simulation of the emergent behavior of small drones swarming for distributed target localization
    Journal of Computational Science, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Nicoletta De Francesco, Massimiliano Lega, Gigliola Vaglini
    Abstract:

    Abstract A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically inspired processes: Stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios.

  • Sleep behavior assessment via smartwatch and stigmergic receptive fields
    Personal and Ubiquitous Computing, 2018
    Co-Authors: Antonio L. Alfeo, Filippo Palumbo, Davide La Rosa, Paolo Barsocchi, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.

  • A Stigmergy-Based Analysis of City Hotspots to Discover Trends and Anomalies in Urban Transportation Usage
    IEEE Transactions on Intelligent Transportation Systems, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Sara Egidi, Bruno Lepri, Gigliola Vaglini
    Abstract:

    A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on Stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing Stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e. hotspots), analyze their activity over time, and unfold anomalies. Furthermore, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

Mario G C A Cimino - One of the best experts on this subject based on the ideXlab platform.

  • design and simulation of the emergent behavior of small drones swarming for distributed target localization
    Journal of Computational Science, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Nicoletta De Francesco, Massimiliano Lega, Gigliola Vaglini
    Abstract:

    Abstract A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically inspired processes: Stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios.

  • Fostering distributed business logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination
    Information Systems Frontiers, 2018
    Co-Authors: Francesco P. Appio, Alessandro Lazzeri, Andrea Lazzeri, Antonella Martini, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assembling business facts together while protecting data source. We propose a privacy-preserving perturbation technique which is based on digital Stigmergy. Stigmergy is a processing schema based on the principle of self-aggregation of marks produced by data. Stigmergy allows protecting data privacy, because only marks are involved in aggregation, in place of actual data values, without explicit data modeling. This paper discusses the proposed approach and examines its characteristics through actual scenarios.

  • Sleep behavior assessment via smartwatch and stigmergic receptive fields
    Personal and Ubiquitous Computing, 2018
    Co-Authors: Antonio L. Alfeo, Filippo Palumbo, Davide La Rosa, Paolo Barsocchi, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.

  • A Stigmergy-Based Analysis of City Hotspots to Discover Trends and Anomalies in Urban Transportation Usage
    IEEE Transactions on Intelligent Transportation Systems, 2018
    Co-Authors: Antonio L. Alfeo, Mario G C A Cimino, Sara Egidi, Bruno Lepri, Gigliola Vaglini
    Abstract:

    A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on Stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing Stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e. hotspots), analyze their activity over time, and unfold anomalies. Furthermore, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

  • Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions
    'MDPI AG', 2018
    Co-Authors: Mario G C A Cimino, Alessandro Lazzeri, Witold Pedrycz, Gigliola Vaglini
    Abstract:

    In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based Stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard’s similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity

Lepri Bruno - One of the best experts on this subject based on the ideXlab platform.

  • Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Alfeo, Antonio Luca, Lepri Bruno, Pentland, Alexander Sandy, Cimino, Mario Gca, Vaglini Gigliola
    Abstract:

    © 2019 IEEE. In Turkey, the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrian refugees, by analyzing a real-world call detail record (CDR) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension

  • Assessing refugees' integration via spatio-temporal similarities of mobility and calling behaviors
    2019
    Co-Authors: Luca Alfeo Antonio, Pentland Alex, Lepri Bruno, Cimino Mario, Vaglini Gigliola
    Abstract:

    In Turkey, the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrian refugees, by analyzing a real-world call detail record (CDR) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension

  • Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy
    2019
    Co-Authors: Alfeo, Antonio Luca, Pentland Alex, Lepri Bruno, Cimino Mario, Vaglini Gigliola
    Abstract:

    The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational Stigmergy. By using computational Stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational Stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only i n certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015

  • Detecting permanent and intermittent purchase hotspots via computational Stigmergy
    'Scitepress', 2019
    Co-Authors: Alfeo Antonio, Lepri Bruno, Cimino Mario, Pentland, Alexander Sandy, Vaglini Gigliola
    Abstract:

    Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational Stigmergy. By using computational Stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational Stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015

  • Stigmergy-based modeling to discover urban activity patterns from positioning data
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Alfeo, Antonio L., Pentland Alex, Lepri Bruno, Cimino, Mario G. C. A., Egidi Sara, Vaglini Gigliola
    Abstract:

    Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on Stigmergy. By using Stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015