Today, globalized markets require more resilient and agile manufacturing systems, as well as cust... more Today, globalized markets require more resilient and agile manufacturing systems, as well as customized and virtualized features. Classical self-standing manufacturing systems are evolving into collaborative networks such as Cloud Manufacturing (based on centralized knowledge and distributed resources) or Shared Manufacturing (based on fully decentralized knowledge and distributed resources) as a solution to ensure business continuity under normal as well as special circumstances. Additive Manufacturing (AM), one of the enablers of Industry 4.0 (I4.0), is a promising technology for innovative production models due to its inherent distributed capabilities, digital nature, and product customization ability. To increase the adaptivity of distributed resources using AM technology, this paper proposes a mechanism for sharing workload and resources under unexpected behaviours in the supply chain. Smart contracts and blockchain technology in this concept are used to provide decentralized, transparent, and trusted operation of such systems, which provide more resilience to disruptive factors. In this paper, the proposed Blockchain-based Shared Additive Manufacturing (BBSAM) protocol, ontology, and workflow for AM capacity pooling are discussed and analysed under special conditions such as anomalous demand. Discrete-time Python simulation on a real Italian AM market dataset, also provided, is available on GitHub.
Feature selection refers to a problem to select a subset of features which are most optimal for i... more Feature selection refers to a problem to select a subset of features which are most optimal for intended tasks. As one of well-known feature selection methods, clustering features into several groups and picking one feature from each group have been used for unsupervised feature selection. Since the purpose of clustering in feature selection is to select a feature from each group, the quality of the feature to be selected should be considered in the clustering process. In this paper, we propose a feature selection method using hierarchical clustering. A new similarity measure between two feature groups is defined by directly using the representative feature in each group. Experimental results show that our method can select good features even for supervised learning.
Purpose: To evaluate the diagnostic performance of Deep Learning (DL) machine for the detection o... more Purpose: To evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study conducted between 1st and 30th April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound skilled trainees and DL machine were asked to make a diagnosis reviewing uterine images. We evaluated and compared the accuracy, sensitivity, positive predictive value, F1- score, specificity and negative predictive value of the DL model and the trainees for adenomyosis diagnosis. Results: Accuracy of DL and intermediate ultrasound skilled trainees for the diagnosis of adenomyosis were 0.51 (95% CI, 0.48-0.54) and 0.70 (95% CI, 0.60-0.79), respectivel...
Proceedings of the 3rd International Conference on Deep Learning Theory and Applications
Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging chal... more Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging challenge. SHM aims to detect and identify any deviation from a reference condition, typically a damage-free baseline, to keep track of the relevant structural integrity. Machine Learning (ML) techniques have recently been employed to empower vibration-based SHM systems. Supervised ML can provide more information than unsupervised ML, but it requires human intervention to appropriately label data describing the nature of the damage. However, labelled data related to damage conditions of civil structures are often unavailable. To overcome this limitation, a key solution is a Digital Twin relying on physics-based numerical models to simulate the structural response in terms of the vibration recordings provided by IoT devices during the events of interest, such as wind or seismic excitations. This paper presents such comprehensive approach to address the damage localization task by exploiting a Convolutional Neural Network (CNN). Early experimental results related to a pilot application involving a sample structure, show the potential of the proposed approach and the reusability of the trained system in presence of varying loading scenarios.
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
This work presents an application of the Perceptually Important Points (PIP) technique for the an... more This work presents an application of the Perceptually Important Points (PIP) technique for the analysis of VLF time series. The aim of the analysis is to detect anomalies with respect to the normal variations of the data trends. Such anomalies could reveal possible radio precursors of the earthquake. Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10–50 kHz) and LF (150–300 kHz) radio precursors of earthquakes. The time series used for experiments was collected during the Dodecanese islands earthquakes ($\text{MW}=5.6$ and $\text{MW}=5.7$) occurred on January 30, 2020.
In nowadays manufacturing, each technical assistance operation is digitally tracked. This results... more In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine ...
Business Processes (BPs) are the key instrument to<br> understand how companies operate at ... more Business Processes (BPs) are the key instrument to<br> understand how companies operate at an organizational level, taking<br> an as-is view of the workflow, and how to address their issues by<br> identifying a to-be model. In last year's, the BP Model and Notation<br> (BPMN) has become a de-facto standard for modeling processes.<br> However, this standard does not incorporate explicitly the Problem-<br> Solving (PS) knowledge in the Process Modeling (PM) results. Thus,<br> such knowledge cannot be shared or reused. To narrow this gap is<br> today a challenging research area. In this paper we present a<br> framework able to capture the PS knowledge and to improve a<br> workflow. This framework extends the BPMN specification by<br> incorporating new general-purpose elements. A pilot scenario is also<br> presented and discussed.
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017
Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capac... more Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy. Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects' coordination mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural parameters are tuned by a differential evolution algorithm. SRFs are used in a multilayer architecture, providing further levels of processing to realize macro analyses in the application domain. As a result, the architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or deviations in performance. As a proof of concept, the approach has been experimented on three subjects.
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017
Smart devices are increasingly used for health monitoring. We present a novel connectionist archi... more Smart devices are increasingly used for health monitoring. We present a novel connectionist architecture to detect elderly behavior shift from data gathered by wearable or ambient sensing technology. Behavior shift is a pattern used in many applications: it may indicate initial signs of disease or deviations in performance. In the proposed architecture, the input samples are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects' coordination mechanism, and is managed by computational units called Stigmergic Receptive Fields (SRFs), which provide a (dis-)similarity measure between sample streams. This paper presents the architectural view, and summarizes the achievements related to three application case studies, i.e., indoor mobility behavior, sleep behavior, and physical activity behavior.
Proceedings of the International Conference on Image Processing and Vision Engineering, 2021
In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or... more In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one. This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2.
Positioning data offer a remarkable source of information to analyze crowds urban dynamics. Howev... more 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 highdensity 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.
2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), 2019
As populations become increasingly aged, health monitoring has gained increasing importance. Rece... more As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behavior shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments.
The increasing volume of urban human mobility data arises unprecedented opportunities to monitor ... more The increasing volume of urban human mobility data arises unprecedented opportunities to monitor and understand city dynamics. Identifying events which do not conform to the expected patterns can enhance the awareness of decision makers for a variety of purposes, such as the management of social events or extreme weather situations [1]. For this purpose GPS-equipped vehicles provide huge amount of reliable data about urban dynamics, exhibiting correlation with human activities, events and city structure [2]. For example, in [3] the impact of a social event is evaluated by analyzing taxi traces data. Here, the authors model typical passenger flow in an area, in order to compute the probability that an event happens. Then, the event impact is measured by analyzing abnormal traffic flows in the area via Discrete Fourier Transform. In [4] GPS trajectories are mapped through an Interactive Voting-based Map Matching Algorithm. This mapping is used for off-line characterization of normal d...
Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019
This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection... more This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.
A current research trend in neurocomputing involves the design of novel artificial neural network... more A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed. Computational stigmergy is used to dynamically increase (or decrease) the strength of a connection, or the activation level, of an artificial neuron when stimulated (or released). This study lays down a basic framework for the derivation of a stigmergic NN with a related training algorithm. To show its potential, some pilot experiments have been reported. The XOR problem is solved by using only one single stigmergic neuron with one input and one output. A static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have been trained to solve the MNIST digits recognition benchmark.
By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host ... more By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host community tensions toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users’ mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigme...
Mining Intelligence and Knowledge Exploration, 2018
Pattern recognition in financial time series is not a trivial task, due to level of noise, volati... more Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on humancentric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out over the FTSE-MIB index.
Today, globalized markets require more resilient and agile manufacturing systems, as well as cust... more Today, globalized markets require more resilient and agile manufacturing systems, as well as customized and virtualized features. Classical self-standing manufacturing systems are evolving into collaborative networks such as Cloud Manufacturing (based on centralized knowledge and distributed resources) or Shared Manufacturing (based on fully decentralized knowledge and distributed resources) as a solution to ensure business continuity under normal as well as special circumstances. Additive Manufacturing (AM), one of the enablers of Industry 4.0 (I4.0), is a promising technology for innovative production models due to its inherent distributed capabilities, digital nature, and product customization ability. To increase the adaptivity of distributed resources using AM technology, this paper proposes a mechanism for sharing workload and resources under unexpected behaviours in the supply chain. Smart contracts and blockchain technology in this concept are used to provide decentralized, transparent, and trusted operation of such systems, which provide more resilience to disruptive factors. In this paper, the proposed Blockchain-based Shared Additive Manufacturing (BBSAM) protocol, ontology, and workflow for AM capacity pooling are discussed and analysed under special conditions such as anomalous demand. Discrete-time Python simulation on a real Italian AM market dataset, also provided, is available on GitHub.
Feature selection refers to a problem to select a subset of features which are most optimal for i... more Feature selection refers to a problem to select a subset of features which are most optimal for intended tasks. As one of well-known feature selection methods, clustering features into several groups and picking one feature from each group have been used for unsupervised feature selection. Since the purpose of clustering in feature selection is to select a feature from each group, the quality of the feature to be selected should be considered in the clustering process. In this paper, we propose a feature selection method using hierarchical clustering. A new similarity measure between two feature groups is defined by directly using the representative feature in each group. Experimental results show that our method can select good features even for supervised learning.
Purpose: To evaluate the diagnostic performance of Deep Learning (DL) machine for the detection o... more Purpose: To evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study conducted between 1st and 30th April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound skilled trainees and DL machine were asked to make a diagnosis reviewing uterine images. We evaluated and compared the accuracy, sensitivity, positive predictive value, F1- score, specificity and negative predictive value of the DL model and the trainees for adenomyosis diagnosis. Results: Accuracy of DL and intermediate ultrasound skilled trainees for the diagnosis of adenomyosis were 0.51 (95% CI, 0.48-0.54) and 0.70 (95% CI, 0.60-0.79), respectivel...
Proceedings of the 3rd International Conference on Deep Learning Theory and Applications
Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging chal... more Structural Health Monitoring (SHM) of civil structures using IoT sensors is a major emerging challenge. SHM aims to detect and identify any deviation from a reference condition, typically a damage-free baseline, to keep track of the relevant structural integrity. Machine Learning (ML) techniques have recently been employed to empower vibration-based SHM systems. Supervised ML can provide more information than unsupervised ML, but it requires human intervention to appropriately label data describing the nature of the damage. However, labelled data related to damage conditions of civil structures are often unavailable. To overcome this limitation, a key solution is a Digital Twin relying on physics-based numerical models to simulate the structural response in terms of the vibration recordings provided by IoT devices during the events of interest, such as wind or seismic excitations. This paper presents such comprehensive approach to address the damage localization task by exploiting a Convolutional Neural Network (CNN). Early experimental results related to a pilot application involving a sample structure, show the potential of the proposed approach and the reusability of the trained system in presence of varying loading scenarios.
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
This work presents an application of the Perceptually Important Points (PIP) technique for the an... more This work presents an application of the Perceptually Important Points (PIP) technique for the analysis of VLF time series. The aim of the analysis is to detect anomalies with respect to the normal variations of the data trends. Such anomalies could reveal possible radio precursors of the earthquake. Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10–50 kHz) and LF (150–300 kHz) radio precursors of earthquakes. The time series used for experiments was collected during the Dodecanese islands earthquakes ($\text{MW}=5.6$ and $\text{MW}=5.7$) occurred on January 30, 2020.
In nowadays manufacturing, each technical assistance operation is digitally tracked. This results... more In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine ...
Business Processes (BPs) are the key instrument to<br> understand how companies operate at ... more Business Processes (BPs) are the key instrument to<br> understand how companies operate at an organizational level, taking<br> an as-is view of the workflow, and how to address their issues by<br> identifying a to-be model. In last year's, the BP Model and Notation<br> (BPMN) has become a de-facto standard for modeling processes.<br> However, this standard does not incorporate explicitly the Problem-<br> Solving (PS) knowledge in the Process Modeling (PM) results. Thus,<br> such knowledge cannot be shared or reused. To narrow this gap is<br> today a challenging research area. In this paper we present a<br> framework able to capture the PS knowledge and to improve a<br> workflow. This framework extends the BPMN specification by<br> incorporating new general-purpose elements. A pilot scenario is also<br> presented and discussed.
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017
Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capac... more Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy. Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects' coordination mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural parameters are tuned by a differential evolution algorithm. SRFs are used in a multilayer architecture, providing further levels of processing to realize macro analyses in the application domain. As a result, the architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or deviations in performance. As a proof of concept, the approach has been experimented on three subjects.
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017
Smart devices are increasingly used for health monitoring. We present a novel connectionist archi... more Smart devices are increasingly used for health monitoring. We present a novel connectionist architecture to detect elderly behavior shift from data gathered by wearable or ambient sensing technology. Behavior shift is a pattern used in many applications: it may indicate initial signs of disease or deviations in performance. In the proposed architecture, the input samples are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects' coordination mechanism, and is managed by computational units called Stigmergic Receptive Fields (SRFs), which provide a (dis-)similarity measure between sample streams. This paper presents the architectural view, and summarizes the achievements related to three application case studies, i.e., indoor mobility behavior, sleep behavior, and physical activity behavior.
Proceedings of the International Conference on Image Processing and Vision Engineering, 2021
In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or... more In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one. This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2.
Positioning data offer a remarkable source of information to analyze crowds urban dynamics. Howev... more 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 highdensity 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.
2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), 2019
As populations become increasingly aged, health monitoring has gained increasing importance. Rece... more As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behavior shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments.
The increasing volume of urban human mobility data arises unprecedented opportunities to monitor ... more The increasing volume of urban human mobility data arises unprecedented opportunities to monitor and understand city dynamics. Identifying events which do not conform to the expected patterns can enhance the awareness of decision makers for a variety of purposes, such as the management of social events or extreme weather situations [1]. For this purpose GPS-equipped vehicles provide huge amount of reliable data about urban dynamics, exhibiting correlation with human activities, events and city structure [2]. For example, in [3] the impact of a social event is evaluated by analyzing taxi traces data. Here, the authors model typical passenger flow in an area, in order to compute the probability that an event happens. Then, the event impact is measured by analyzing abnormal traffic flows in the area via Discrete Fourier Transform. In [4] GPS trajectories are mapped through an Interactive Voting-based Map Matching Algorithm. This mapping is used for off-line characterization of normal d...
Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019
This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection... more This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.
A current research trend in neurocomputing involves the design of novel artificial neural network... more A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed. Computational stigmergy is used to dynamically increase (or decrease) the strength of a connection, or the activation level, of an artificial neuron when stimulated (or released). This study lays down a basic framework for the derivation of a stigmergic NN with a related training algorithm. To show its potential, some pilot experiments have been reported. The XOR problem is solved by using only one single stigmergic neuron with one input and one output. A static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have been trained to solve the MNIST digits recognition benchmark.
By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host ... more By absorbing more than 3.4 million Syrians, Turkey has shown remarkable resilience. But the host community tensions toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users’ mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigme...
Mining Intelligence and Knowledge Exploration, 2018
Pattern recognition in financial time series is not a trivial task, due to level of noise, volati... more Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on humancentric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out over the FTSE-MIB index.
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Papers by Mario G C A Cimino