activity recognition ambient assisted living biomedical data clustering cognitive robotics competitive repetition suppression learning deep learning Echo state networks feature selection fuzzy graph matching fuzzy reasoning generative model graphical models hidden tree Markov model kernel methods multivariate time-series neural networks pervasive computing planning recurrent neural network reservoir computing robotic ecology service matchmaking statistics structured data processing support vector machine tree kernel tree structured data unsupervised learning wireless sensor networks
2019 |
Cosimo, Della Santina; Visar, Arapi; Giuseppe, Averta; Francesca, Damiani; Gaia, Fiore; Alessandro, Settimi; Giuseppe, Catalano Manuel; Davide, Bacciu; Antonio, Bicchi; Matteo, Bianchi Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands Journal Article Forthcoming IEEE Robotics and Automation Letters, Forthcoming. BibTeX | Tags: convolutional neural network, deep learning, image understanding, machine vision, recurrent neural network, robotics @article{ral2019, title = {Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands}, author = {Della Santina Cosimo and Arapi Visar and Averta Giuseppe and Damiani Francesca and Fiore Gaia and Settimi Alessandro and Catalano Manuel Giuseppe and Bacciu Davide and Bicchi Antonio and Bianchi Matteo}, year = {2019}, date = {2019-01-11}, journal = {IEEE Robotics and Automation Letters}, keywords = {convolutional neural network, deep learning, image understanding, machine vision, recurrent neural network, robotics}, pubstate = {forthcoming}, tppubtype = {article} } |
Davide, Bacciu; Antonio, Bruno Deep Tree Transductions - A Short Survey Conference Forthcoming Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) , Forthcoming. Abstract | BibTeX | Tags: deep learning, recurrent neural network, structured data processing, tree structured data @conference{inns2019, title = {Deep Tree Transductions - A Short Survey}, author = {Bacciu Davide and Bruno Antonio}, year = {2019}, date = {2019-01-04}, booktitle = {Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) }, abstract = {The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.}, keywords = {deep learning, recurrent neural network, structured data processing, tree structured data}, pubstate = {forthcoming}, tppubtype = {conference} } The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems. |
2018 |
Davide, Bacciu; Daniele, Castellana Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering Journal Article Forthcoming Neurocomputing (Accepted), Forthcoming. BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning @article{neucomBayesHTMM, title = {Bayesian Mixtures of Hidden Tree Markov Models for Structured Data Clustering}, author = {Bacciu Davide and Castellana Daniele}, year = {2018}, date = {2018-12-31}, journal = {Neurocomputing (Accepted)}, keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning}, pubstate = {forthcoming}, tppubtype = {article} } |
Arapi, Visar; Santina, Cosimo Della; Bacciu, Davide; Bianchi, Matteo; Bicchi, Antonio DeepDynamicHand: A deep neural architecture for labeling hand manipulation strategies in video sources exploiting temporal information Journal Article Frontiers in Neurorobotics, 12 , pp. 86, 2018. Abstract | Links | BibTeX | Tags: convolutional neural network, deep learning, image understanding, machine vision, recurrent neural network, robotics @article{frontNeurob18, title = {DeepDynamicHand: A deep neural architecture for labeling hand manipulation strategies in video sources exploiting temporal information }, author = {Visar Arapi and Cosimo Della Santina and Davide Bacciu and Matteo Bianchi and Antonio Bicchi}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2018.00086/full}, doi = {10.3389/fnbot.2018.00086}, year = {2018}, date = {2018-12-17}, journal = {Frontiers in Neurorobotics}, volume = {12}, pages = {86}, abstract = {Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side. However, current approaches tend to neglect the dynamic nature of hand pose recognition problems, limiting the effectiveness of these techniques in identifying sequences of manipulation primitives underpinning action generation, e.g. during purposeful interaction with the environment. In this work, we propose a vision-based supervised Hand Pose Recognition method which, for the first time, takes into account temporal information to identify meaningful sequences of actions in grasping and manipulation tasks . More specifically, we apply Deep Neural Networks to automatically learn features from hand posture images that consist of frames extracted from grasping and manipulation task videos with objects and external environmental constraints. For training purposes, videos are divided into intervals, each associated to a specific action by a human supervisor. The proposed algorithm combines a Convolutional Neural Network to detect the hand within each video frame and a Recurrent Neural Network to predict the hand action in the current frame, while taking into consideration the history of actions performed in the previous frames. Experimental validation has been performed on two datasets of dynamic hand-centric strategies, where subjects regularly interact with objects and environment. Proposed architecture achieved a very good classification accuracy on both datasets, reaching performance up to 94%, and outperforming state of the art techniques. The outcomes of this study can be successfully applied to robotics, e.g for planning and control of soft anthropomorphic manipulators. }, keywords = {convolutional neural network, deep learning, image understanding, machine vision, recurrent neural network, robotics}, pubstate = {published}, tppubtype = {article} } Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side. However, current approaches tend to neglect the dynamic nature of hand pose recognition problems, limiting the effectiveness of these techniques in identifying sequences of manipulation primitives underpinning action generation, e.g. during purposeful interaction with the environment. In this work, we propose a vision-based supervised Hand Pose Recognition method which, for the first time, takes into account temporal information to identify meaningful sequences of actions in grasping and manipulation tasks . More specifically, we apply Deep Neural Networks to automatically learn features from hand posture images that consist of frames extracted from grasping and manipulation task videos with objects and external environmental constraints. For training purposes, videos are divided into intervals, each associated to a specific action by a human supervisor. The proposed algorithm combines a Convolutional Neural Network to detect the hand within each video frame and a Recurrent Neural Network to predict the hand action in the current frame, while taking into consideration the history of actions performed in the previous frames. Experimental validation has been performed on two datasets of dynamic hand-centric strategies, where subjects regularly interact with objects and environment. Proposed architecture achieved a very good classification accuracy on both datasets, reaching performance up to 94%, and outperforming state of the art techniques. The outcomes of this study can be successfully applied to robotics, e.g for planning and control of soft anthropomorphic manipulators. |
Davide, Bacciu; Antonio, Bruno Text Summarization as Tree Transduction by Top-Down TreeLSTM Conference Forthcoming Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18), IEEE, Forthcoming. Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, neural networks, structured data processing, tree structured data, tree transductions @conference{ssci2018, title = {Text Summarization as Tree Transduction by Top-Down TreeLSTM}, author = {Bacciu Davide and Bruno Antonio}, url = {https://arxiv.org/abs/1809.09096}, year = {2018}, date = {2018-11-18}, booktitle = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18)}, publisher = {IEEE}, abstract = {Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. }, keywords = {deep learning, deep learning for graphs, neural networks, structured data processing, tree structured data, tree transductions}, pubstate = {forthcoming}, tppubtype = {conference} } Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. |
Davide, Bacciu; Antonio, Carta; Alessandro, Sperduti Linear Memory Networks Unpublished 2018. Abstract | Links | BibTeX | Tags: deep learning, recurrent neural network @unpublished{lmnArx18, title = {Linear Memory Networks}, author = {Bacciu Davide and Carta Antonio and Sperduti Alessandro}, url = {https://arxiv.org/pdf/1811.03356.pdf}, year = {2018}, date = {2018-11-09}, organization = {Arxiv}, abstract = {Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models. }, keywords = {deep learning, recurrent neural network}, pubstate = {published}, tppubtype = {unpublished} } Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models. |
Marco, Podda; Davide, Bacciu; Alessio, Micheli; Roberto, Bellu; Giulia, Placidi; Luigi, Gagliardi A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor Journal Article Nature Scientific Reports, 8 , 2018. Abstract | Links | BibTeX | Tags: bioinformatics, biomedical data, neural networks, support vector machine @article{naturescirep2018, title = {A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor}, author = {Podda Marco and Bacciu Davide and Micheli Alessio and Bellu Roberto and Placidi Giulia and Gagliardi Luigi }, url = {https://doi.org/10.1038/s41598-018-31920-6}, doi = {10.1038/s41598-018-31920-6}, year = {2018}, date = {2018-09-13}, journal = {Nature Scientific Reports}, volume = {8}, abstract = {Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community.}, keywords = {bioinformatics, biomedical data, neural networks, support vector machine}, pubstate = {published}, tppubtype = {article} } Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community. |
Davide, Bacciu; Daniele, Castellana Learning Tree Distributions by Hidden Markov Models Workshop Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18), 2018. Links | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data @workshop{learnaut18, title = {Learning Tree Distributions by Hidden Markov Models}, author = {Bacciu Davide and Castellana Daniele}, editor = {Rémi Eyraud and Jeffrey Heinz and Guillaume Rabusseau and Matteo Sammartino }, url = {https://arxiv.org/abs/1805.12372}, year = {2018}, date = {2018-07-13}, booktitle = {Proceedings of the FLOC 2018 Workshop on Learning and Automata (LearnAut'18)}, keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data}, pubstate = {published}, tppubtype = {workshop} } |
Davide, Bacciu; Michele, Colombo; Davide, Morelli; David, Plans Randomized neural networks for preference learning with physiological data Journal Article Neurocomputing, 298 , pp. 9-20, 2018. Abstract | Links | BibTeX | Tags: activity recognition, biomedical data, Echo state networks, preference learning, randomized networks, reservoir computing @article{neurocomp2017, title = {Randomized neural networks for preference learning with physiological data}, author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David}, editor = {Fabio Aiolli and Luca Oneto and Michael Biehl }, url = {https://authors.elsevier.com/a/1Wxbz_L2Otpsb3}, doi = {10.1016/j.neucom.2017.11.070}, year = {2018}, date = {2018-07-12}, journal = {Neurocomputing}, volume = {298}, pages = {9-20}, abstract = {The paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation.}, keywords = {activity recognition, biomedical data, Echo state networks, preference learning, randomized networks, reservoir computing}, pubstate = {published}, tppubtype = {article} } The paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation. |
Davide, Bacciu; Federico, Errica; Alessio, Micheli Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing Conference Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018. Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing @conference{icml2018, title = {Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing}, author = {Bacciu Davide and Errica Federico and Micheli Alessio}, url = {https://arxiv.org/abs/1805.10636}, year = {2018}, date = {2018-07-11}, booktitle = {Proceedings of the 35th International Conference on Machine Learning (ICML 2018)}, keywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Andrea, Bongiorno Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs Conference Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN 2018) , IEEE, 2018. Links | BibTeX | Tags: deep learning, Echo state networks, reservoir computing @conference{ijcnn2018, title = {Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs}, author = {Bacciu Davide and Bongiorno Andrea}, url = {https://arxiv.org/abs/1805.09244}, year = {2018}, date = {2018-07-09}, booktitle = {Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN 2018) }, pages = {1-9}, publisher = {IEEE}, keywords = {deep learning, Echo state networks, reservoir computing}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Daniele, Castellana Mixture of Hidden Markov Models as Tree Encoder Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18), i6doc.com, Louvain-la-Neuve, Belgium, 2018, ISBN: 978-287587047-6. Abstract | BibTeX | Tags: graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning @conference{esann2018Tree, title = {Mixture of Hidden Markov Models as Tree Encoder}, author = {Bacciu Davide and Castellana Daniele}, editor = {Michel Verleysen}, isbn = {978-287587047-6}, year = {2018}, date = {2018-04-26}, booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18)}, pages = {543-548}, publisher = {i6doc.com}, address = {Louvain-la-Neuve, Belgium}, abstract = {The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.}, keywords = {graphical models, hidden tree Markov model, structured data processing, tree structured data, unsupervised learning}, pubstate = {published}, tppubtype = {conference} } The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classification tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation. |
Davide, Bacciu; JG, Lisboa Paulo; D, Martin Jose; Ruxandra, Stoean; Alfredo, Vellido Bioinformatics and medicine in the era of deep learning Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18), i6doc.com, Louvain-la-Neuve, Belgium, 2018, ISBN: 978-287587047-6. Abstract | Links | BibTeX | Tags: bioinformatics, biomedical data, deep learning @conference{esann2018Tut, title = {Bioinformatics and medicine in the era of deep learning}, author = {Bacciu Davide and Lisboa Paulo JG and Martin Jose D and Stoean Ruxandra and Vellido Alfredo}, editor = {Michel Verleysen}, url = {http://arxiv.org/abs/1802.09791}, isbn = {978-287587047-6}, year = {2018}, date = {2018-04-26}, booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18)}, pages = {345-354}, publisher = {i6doc.com}, address = {Louvain-la-Neuve, Belgium}, abstract = {Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.}, keywords = {bioinformatics, biomedical data, deep learning}, pubstate = {published}, tppubtype = {conference} } Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic. |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Generative Kernels for Tree-Structured Data Journal Article Neural Networks and Learning Systems, IEEE Transactions on, 2018, ISSN: 2162-2388 . Abstract | Links | BibTeX | Tags: hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data @article{tnnlsTreeKer17, title = {Generative Kernels for Tree-Structured Data}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro}, doi = {10.1109/TNNLS.2017.2785292}, issn = {2162-2388 }, year = {2018}, date = {2018-01-15}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, abstract = {The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.}, keywords = {hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data}, pubstate = {published}, tppubtype = {article} } The paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Further, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. The paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping. |
2017 |
Davide, Bacciu Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data Conference Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17), IEEE, 2017. Links | BibTeX | Tags: deep learning, hidden tree Markov model, structured data processing @conference{dl2017, title = {Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data}, author = {Bacciu Davide}, url = {https://arxiv.org/abs/1711.07784}, year = {2017}, date = {2017-11-27}, booktitle = {Proc. of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI'17)}, publisher = {IEEE}, keywords = {deep learning, hidden tree Markov model, structured data processing}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli On the Need of Machine Learning as a Service for the Internet of Things Conference To appear in the Proc. of the International Conference on Internet of Things and Machine Learning (IML 2017), International Conference Proceedings Series (ICPS) ACM, 2017, ISBN: 978-1-4503-5243-7. BibTeX | Tags: internet of things, pervasive computing, reservoir computing @conference{iml2017, title = {On the Need of Machine Learning as a Service for the Internet of Things}, author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio}, isbn = {978-1-4503-5243-7}, year = {2017}, date = {2017-10-18}, booktitle = {To appear in the Proc. of the International Conference on Internet of Things and Machine Learning (IML 2017)}, journal = {Proc}, publisher = {ACM}, series = {International Conference Proceedings Series (ICPS)}, keywords = {internet of things, pervasive computing, reservoir computing}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli; Luca, Pedrelli; Erina, Ferro; Luigi, Fortunati; Davide, La Rosa; Filippo, Palumbo; Federico, Vozzi; Oberdan, Parodi A Learning System for Automatic Berg Balance Scale Score Estimation Journal Article Engineering Applications of Artificial Intelligence journal, 66 , pp. 60-74, 2017. Abstract | Links | BibTeX | Tags: ambient assisted living, Echo state networks, multivariate time-series, reservoir computing @article{eaai2017, title = {A Learning System for Automatic Berg Balance Scale Score Estimation}, author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Pedrelli Luca and Ferro Erina and Fortunati Luigi and La Rosa Davide and Palumbo Filippo and Vozzi Federico and Parodi Oberdan}, url = {http://www.sciencedirect.com/science/article/pii/S0952197617302026}, doi = {https://doi.org/10.1016/j.engappai.2017.08.018}, year = {2017}, date = {2017-08-24}, journal = {Engineering Applications of Artificial Intelligence journal}, volume = {66}, pages = {60-74}, abstract = {The objective of this work is the development of a learning system for the automatic assessment of balance abilities in elderly people. The system is based on estimating the Berg Balance Scale (BBS) score from the stream of sensor data gathered by a Wii Balance Board. The scientific challenge tackled by our investigation is to assess the feasibility of exploiting the richness of the temporal signals gathered by the balance board for inferring the complete BBS score based on data from a single BBS exercise. The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ≈≈10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user’s required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models. Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage.}, keywords = {ambient assisted living, Echo state networks, multivariate time-series, reservoir computing}, pubstate = {published}, tppubtype = {article} } The objective of this work is the development of a learning system for the automatic assessment of balance abilities in elderly people. The system is based on estimating the Berg Balance Scale (BBS) score from the stream of sensor data gathered by a Wii Balance Board. The scientific challenge tackled by our investigation is to assess the feasibility of exploiting the richness of the temporal signals gathered by the balance board for inferring the complete BBS score based on data from a single BBS exercise. The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ≈≈10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user’s required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models. Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage. |
Ovidiu, Vermesan; Arne, Broring; Elias, Tragos; Martin, Serrano; Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli; Mauro, Dragone; Alessandro, Saffiotti; Pieter, Simoens; Filippo, Cavallo; Roy, Bahr Vermesan, Ovidiu; Bacquet, Joel (Ed.): Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution, Chapter 4, pp. 97-155, River Publishers, 2017, ISBN: 9788793609105. Links | BibTeX | Tags: internet of things, pervasive computing, reservoir computing | @inbook{iotBook17, title = {Internet of Robotic Things - Converging Sensing/Actuating, Hyperconnectivity, Artificial Intelligence and IoT Platforms}, author = {Vermesan Ovidiu and Broring Arne and Tragos Elias and Serrano Martin and Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Dragone Mauro and Saffiotti Alessandro and Simoens Pieter and Cavallo Filippo and Bahr Roy}, editor = {Ovidiu Vermesan and Joel Bacquet}, url = {http://www.riverpublishers.com/downloadchapter.php?file=RP_9788793609105C4.pdf}, doi = {10.13052/rp-9788793609105}, isbn = {9788793609105}, year = {2017}, date = {2017-06-28}, booktitle = {Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution}, pages = {97-155}, publisher = {River Publishers}, chapter = {4}, keywords = {internet of things, pervasive computing, reservoir computing |}, pubstate = {published}, tppubtype = {inbook} } |
Filippo, Palumbo; Davide, La Rosa; Erina, Ferro; Davide, Bacciu; Claudio, Gallicchio; Alession, Micheli; Stefano, Chessa; Federico, Vozzi; Oberdan, Parodi Reliability and human factors in Ambient Assisted Living environments: The DOREMI case study Journal Article Journal of Reliable Intelligent Environments, 3 (3), pp. 139–157, 2017, ISBN: 2199-4668. Abstract | Links | BibTeX | Tags: activity recognition, ambient assisted living, biomedical data, pervasive computing, reservoir computing @article{jrie2017, title = {Reliability and human factors in Ambient Assisted Living environments: The DOREMI case study}, author = {Palumbo Filippo and La Rosa Davide and Ferro Erina and Bacciu Davide and Gallicchio Claudio and Micheli Alession and Chessa Stefano and Vozzi Federico and Parodi Oberdan}, doi = {10.1007/s40860-017-0042-1}, isbn = {2199-4668}, year = {2017}, date = {2017-06-17}, journal = {Journal of Reliable Intelligent Environments}, volume = {3}, number = {3}, pages = {139–157}, publisher = {Springer}, abstract = {Malnutrition, sedentariness, and cognitive decline in elderly people represent the target areas addressed by the DOREMI project. It aimed at developing a systemic solution for elderly, able to prolong their functional and cognitive capacity by empowering, stimulating, and unobtrusively monitoring the daily activities according to well-defined “Active Ageing” life-style protocols. Besides the key features of DOREMI in terms of technological and medical protocol solutions, this work is focused on the analysis of the impact of such a solution on the daily life of users and how the users’ behaviour modifies the expected results of the system in a long-term perspective. To this end, we analyse the reliability of the whole system in terms of human factors and their effects on the reliability requirements identified before starting the experimentation in the pilot sites. After giving an overview of the technological solutions we adopted in the project, this paper concentrates on the activities conducted during the two pilot site studies (32 test sites across UK and Italy), the users’ experience of the entire system, and how human factors influenced its overall reliability.}, keywords = {activity recognition, ambient assisted living, biomedical data, pervasive computing, reservoir computing}, pubstate = {published}, tppubtype = {article} } Malnutrition, sedentariness, and cognitive decline in elderly people represent the target areas addressed by the DOREMI project. It aimed at developing a systemic solution for elderly, able to prolong their functional and cognitive capacity by empowering, stimulating, and unobtrusively monitoring the daily activities according to well-defined “Active Ageing” life-style protocols. Besides the key features of DOREMI in terms of technological and medical protocol solutions, this work is focused on the analysis of the impact of such a solution on the daily life of users and how the users’ behaviour modifies the expected results of the system in a long-term perspective. To this end, we analyse the reliability of the whole system in terms of human factors and their effects on the reliability requirements identified before starting the experimentation in the pilot sites. After giving an overview of the technological solutions we adopted in the project, this paper concentrates on the activities conducted during the two pilot site studies (32 test sites across UK and Italy), the users’ experience of the entire system, and how human factors influenced its overall reliability. |
Davide, Bacciu; Francesco, Crecchi; Davide, Morelli DropIn: Making Neural Networks Robust to Missing Inputs by Dropout Conference Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) , IEEE, 2017, ISBN: 978-1-5090-6182-2. Abstract | Links | BibTeX | Tags: ambient assisted living, deep learning, Echo state networks, recurrent neural network, reservoir computing @conference{ijcnn2017, title = {DropIn: Making Neural Networks Robust to Missing Inputs by Dropout}, author = {Bacciu Davide and Crecchi Francesco and Morelli Davide}, url = {https://arxiv.org/abs/1705.02643}, doi = {10.1109/IJCNN.2017.7966106}, isbn = {978-1-5090-6182-2}, year = {2017}, date = {2017-05-19}, booktitle = {Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) }, pages = {2080-2087}, publisher = {IEEE}, abstract = {The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%–50% of the inputs are not available.}, keywords = {ambient assisted living, deep learning, Echo state networks, recurrent neural network, reservoir computing}, pubstate = {published}, tppubtype = {conference} } The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%–50% of the inputs are not available. |
Davide, Bacciu; Michele, Colombo; Davide, Morelli; David, Plans ELM Preference Learning for Physiological Data Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'17), i6doc.com, Louvain-la-Neuve, Belgium, 2017, ISBN: 978-2-875870384. BibTeX | Tags: activity recognition, biomedical data, preference learning, randomized networks @conference{esann2017, title = {ELM Preference Learning for Physiological Data}, author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David}, editor = {Michel Verleysen}, isbn = {978-2-875870384}, year = {2017}, date = {2017-04-28}, booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'17)}, pages = {99-104}, publisher = {i6doc.com}, address = {Louvain-la-Neuve, Belgium}, keywords = {activity recognition, biomedical data, preference learning, randomized networks}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems Journal Article Journal of Logical and Algebraic Methods in Programming , 87 , pp. 52-66, 2017, ISSN: 2352-2208. Abstract | Links | BibTeX | Tags: big data, bike sharing system, kernel methods, software engineering, support vector machine @article{jlamp2016, title = {An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems}, author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura}, editor = {Alberto Lluch Lafuente and Maurice ter Beek}, doi = {10.1016/j.jlamp.2016.11.002}, issn = {2352-2208}, year = {2017}, date = {2017-01-01}, journal = { Journal of Logical and Algebraic Methods in Programming }, volume = {87}, pages = {52-66}, publisher = {Elsevier}, abstract = {Bike-sharing systems (BSS) are a means of smart transportation with the benefit of a positive impact on urban mobility. To improve the satisfaction of a user of a BSS, it is useful to inform her/him on the status of the stations at run time, and indeed most of the current systems provide the information in terms of number of bicycles parked in each docking stations by means of services available via web. However, when the departure station is empty, the user could also be happy to know how the situation will evolve and, in particular, if a bike is going to arrive (and vice versa when the arrival station is full). To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions.}, keywords = {big data, bike sharing system, kernel methods, software engineering, support vector machine}, pubstate = {published}, tppubtype = {article} } Bike-sharing systems (BSS) are a means of smart transportation with the benefit of a positive impact on urban mobility. To improve the satisfaction of a user of a BSS, it is useful to inform her/him on the status of the stations at run time, and indeed most of the current systems provide the information in terms of number of bicycles parked in each docking stations by means of services available via web. However, when the departure station is empty, the user could also be happy to know how the situation will evolve and, in particular, if a bike is going to arrive (and vice versa when the arrival station is full). To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions. |
2016 |
Davide, Bacciu; Stefano, Chessa; Erina, Ferro; Luigi, Fortunati; Claudio, Gallicchio; Davide, La Rosa; Miguel, Llorente; Alessio, Micheli; Filippo, Palumbo; Oberdan, Parodi; Andrea, Valenti; Federico, Vozzi Detecting socialization events in ageing people: the experienze of the DOREMI project Conference Proceedings of the IEEE 12th International Conference on Intelligent Environments (IE 2016), , IEEE, UK, London, 2016, ISSN: 2472-7571 . Abstract | Links | BibTeX | Tags: activity recognition, ambient assisted living, pervasive computing, wireless sensor networks @conference{ie2016, title = { Detecting socialization events in ageing people: the experienze of the DOREMI project}, author = {Bacciu Davide and Chessa Stefano and Ferro Erina and Fortunati Luigi and Gallicchio Claudio and La Rosa Davide and Llorente Miguel and Micheli Alessio and Palumbo Filippo and Parodi Oberdan and Valenti Andrea and Vozzi Federico}, doi = {10.1109/IE.2016.28}, issn = {2472-7571 }, year = {2016}, date = {2016-10-27}, booktitle = {Proceedings of the IEEE 12th International Conference on Intelligent Environments (IE 2016), }, pages = {132-135}, publisher = {IEEE}, address = {UK, London}, abstract = {The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project.}, keywords = {activity recognition, ambient assisted living, pervasive computing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project. |
Davide, Bacciu Unsupervised feature selection for sensor time-series in pervasive computing applications Journal Article Neural Computing and Applications, 27 (5), pp. 1077-1091, 2016, ISSN: 1433-3058. Abstract | Links | BibTeX | Tags: ambient assisted living, Echo state networks, feature selection, multivariate time-series, pervasive computing, structured data processing, wireless sensor networks @article{icfNca15, title = {Unsupervised feature selection for sensor time-series in pervasive computing applications}, author = {Bacciu Davide}, url = {http://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/nca2015.pdf}, doi = {10.1007/s00521-015-1924-x}, issn = {1433-3058}, year = {2016}, date = {2016-07-01}, journal = {Neural Computing and Applications}, volume = {27}, number = {5}, pages = {1077-1091}, publisher = {Springer London}, abstract = {The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.}, keywords = {ambient assisted living, Echo state networks, feature selection, multivariate time-series, pervasive computing, structured data processing, wireless sensor networks}, pubstate = {published}, tppubtype = {article} } The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks. |
Davide, Bacciu; Vincenzo, Gervasi; Giuseppe, Prencipe An Investigation into Cybernetic Humor, or: Can Machines Laugh? Conference Proceedings of the 8th International Conference on Fun with Algorithms (FUN'16) , 49 , Leibniz International Proceedings in Informatics (LIPIcs) Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2016, ISSN: 1868-8969. Abstract | Links | BibTeX | Tags: deep learning, natural language, recurrent neural network, structured data processing @conference{fun2016, title = {An Investigation into Cybernetic Humor, or: Can Machines Laugh?}, author = {Bacciu Davide and Gervasi Vincenzo and Prencipe Giuseppe}, editor = {Erik D. Demaine and Fabrizio Grandoni}, url = {http://drops.dagstuhl.de/opus/volltexte/2016/5882}, doi = {10.4230/LIPIcs.FUN.2016.3}, issn = {1868-8969}, year = {2016}, date = {2016-06-10}, booktitle = {Proceedings of the 8th International Conference on Fun with Algorithms (FUN'16) }, volume = {49}, pages = {1-15}, publisher = {Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, abstract = {The mechanisms of humour have been the subject of much study and investigation, starting with and up to our days. Much of this work is based on literary theories, put forward by some of the most eminent philosophers and thinkers of all times, or medical theories, investigating the impact of humor on brain activity or behaviour. Recent functional neuroimaging studies, for instance, have investigated the process of comprehending and appreciating humor by examining functional activity in distinctive regions of brains stimulated by joke corpora. Yet, there is precious little work on the computational side, possibly due to the less hilarious nature of computer scientists as compared to men of letters and sawbones. In this paper, we set to investigate whether literary theories of humour can stand the test of algorithmic laughter. Or, in other words, we ask ourselves the vexed question: Can machines laugh? We attempt to answer that question by testing whether an algorithm - namely, a neural network - can "understand" humour, and in particular whether it is possible to automatically identify abstractions that are predicted to be relevant by established literary theories about the mechanisms of humor. Notice that we do not focus here on distinguishing humorous from serious statements - a feat that is clearly way beyond the capabilities of the average human voter, not to mention the average machine - but rather on identifying the underlying mechanisms and triggers that are postulated to exist by literary theories, by verifying if similar mechanisms can be learned by machines. }, keywords = {deep learning, natural language, recurrent neural network, structured data processing}, pubstate = {published}, tppubtype = {conference} } The mechanisms of humour have been the subject of much study and investigation, starting with and up to our days. Much of this work is based on literary theories, put forward by some of the most eminent philosophers and thinkers of all times, or medical theories, investigating the impact of humor on brain activity or behaviour. Recent functional neuroimaging studies, for instance, have investigated the process of comprehending and appreciating humor by examining functional activity in distinctive regions of brains stimulated by joke corpora. Yet, there is precious little work on the computational side, possibly due to the less hilarious nature of computer scientists as compared to men of letters and sawbones. In this paper, we set to investigate whether literary theories of humour can stand the test of algorithmic laughter. Or, in other words, we ask ourselves the vexed question: Can machines laugh? We attempt to answer that question by testing whether an algorithm - namely, a neural network - can "understand" humour, and in particular whether it is possible to automatically identify abstractions that are predicted to be relevant by established literary theories about the mechanisms of humor. Notice that we do not focus here on distinguishing humorous from serious statements - a feat that is clearly way beyond the capabilities of the average human voter, not to mention the average machine - but rather on identifying the underlying mechanisms and triggers that are postulated to exist by literary theories, by verifying if similar mechanisms can be learned by machines. |
Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Alessio, Micheli; Arantxa, Renteria; Claudio, Vairo A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living Conference Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16), 476 , Advances in Intelligent Systems and Computing Springer, 2016, ISBN: 978-3-319-40113-3. Abstract | Links | BibTeX | Tags: activity recognition, ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, robotic ecology, structured data processing, wireless sensor networks @conference{Amato2016, title = {A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living}, author = {Amato Giuseppe and Bacciu Davide and Chessa Stefano and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Micheli Alessio and Renteria Arantxa and Vairo Claudio}, doi = {10.1007/978-3-319-40114-0_1}, isbn = {978-3-319-40113-3}, year = {2016}, date = {2016-06-03}, booktitle = {Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16)}, volume = {476}, pages = {1-9}, publisher = {Springer}, series = {Advances in Intelligent Systems and Computing}, abstract = {We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.}, keywords = {activity recognition, ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, robotic ecology, structured data processing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset. |
Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli A reservoir activation kernel for trees Conference Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16), i6doc.com, 2016, ISBN: 978-287587027-. Links | BibTeX | Tags: Echo state networks, kernel methods, reservoir computing, structured data processing, tree kernel, tree structured data @conference{esann2016, title = {A reservoir activation kernel for trees}, author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio }, editor = {M. Verleysen}, url = {https://www.researchgate.net/profile/Claudio_Gallicchio/publication/313236954_A_Reservoir_Activation_Kernel_for_Trees/links/58a9db0892851cf0e3c6b8df/A-Reservoir-Activation-Kernel-for-Trees.pdf}, isbn = {978-287587027-}, year = {2016}, date = {2016-04-29}, booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'16)}, pages = {29-34}, publisher = { i6doc.com}, keywords = {Echo state networks, kernel methods, reservoir computing, structured data processing, tree kernel, tree structured data}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini Adopting a Machine Learning Approach in the Design of Smart Transportation Systems Online van der Me, Rob; Shashaj, Ariona (Ed.): ERCIM News Magazine 2016. Links | BibTeX | Tags: big data, bike sharing system, kernel methods, software engineering, support vector machine @online{ercim2016, title = {Adopting a Machine Learning Approach in the Design of Smart Transportation Systems}, author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura }, editor = {Rob van der Me and Ariona Shashaj}, url = {http://ercim-news.ercim.eu/en105/special/adopting-a-machine-learning-approach-in-the-design-of-smart-transportation-systems}, issn = {0926-4981 }, year = {2016}, date = {2016-04-01}, organization = {ERCIM News Magazine}, keywords = {big data, bike sharing system, kernel methods, software engineering, support vector machine}, pubstate = {published}, tppubtype = {online} } |
2015 |
Oberdan, Parodi; Federico, Vozzi; Erina, Ferro; Luigi, Fortunati; Alessio, Micheli; Claudio, Gallicchio; Davide, Bacciu; Stefano, Chessa; Antonio, Ascolese Preventing cognitive decline, sedentariness and malnutrition: the DOREMI approach Presentation 29.10.2015, (Palermo, October 29-30, 2015). BibTeX | Tags: activity recognition, ambient assisted living @misc{icities2015, title = {Preventing cognitive decline, sedentariness and malnutrition: the DOREMI approach}, author = {Parodi Oberdan and Vozzi Federico and Ferro Erina and Fortunati Luigi and Micheli Alessio and Gallicchio Claudio and Bacciu Davide and Chessa Stefano and Ascolese Antonio}, year = {2015}, date = {2015-10-29}, booktitle = {The CINI Annual Workshop on ICT for Smart Cities and Communities (I-CiTies 2015)}, note = {Palermo, October 29-30, 2015}, keywords = {activity recognition, ambient assisted living}, pubstate = {published}, tppubtype = {presentation} } |
Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Martin, McGinnity T; Alessio, Micheli; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo; Philip, Vance Robotic Ubiquitous Cognitive Ecology for Smart Homes Journal Article Journal of Intelligent & Robotic Systems, 80 (1), pp. 57-81, 2015, ISSN: 0921-0296. Abstract | Links | BibTeX | Tags: activity recognition, ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, networked robotics, pervasive computing, planning, reservoir computing, robotic ecology, structured data processing, wireless sensor networks @article{bacciuJirs15, title = {Robotic Ubiquitous Cognitive Ecology for Smart Homes}, author = {Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and McGinnity T Martin and Micheli Alessio and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio and Vance Philip}, url = {http://dx.doi.org/10.1007/s10846-015-0178-2}, doi = {10.1007/s10846-015-0178-2}, issn = {0921-0296}, year = {2015}, date = {2015-01-01}, journal = {Journal of Intelligent & Robotic Systems}, volume = {80}, number = {1}, pages = {57-81}, publisher = {Springer Netherlands}, abstract = {Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.}, keywords = {activity recognition, ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, networked robotics, pervasive computing, planning, reservoir computing, robotic ecology, structured data processing, wireless sensor networks}, pubstate = {published}, tppubtype = {article} } Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work. |
Mauro, Dragone; Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Liam, Maguire; Martin, McGinnity; Alessio, Micheli; M.P., O'Hare Gregory; Arantxa, Renteria; Alessandro, Saffiotti; Claudio, Vairo; Philip, Vance A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems Journal Article Engineering Applications of Artificial Intelligence, 45 (C), pp. 269–280, 2015, ISSN: 0952-1976. Links | BibTeX | Tags: ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, pervasive computing, planning, reservoir computing, robotic ecology, wireless sensor networks @article{Dragone:2015:CRE:2827370.2827596, title = {A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems}, author = {Dragone Mauro and Amato Giuseppe and Bacciu Davide and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Maguire Liam and McGinnity Martin and Micheli Alessio and O'Hare Gregory M.P. and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio and Vance Philip}, url = {http://dx.doi.org/10.1016/j.engappai.2015.07.004}, doi = {10.1016/j.engappai.2015.07.004}, issn = {0952-1976}, year = {2015}, date = {2015-01-01}, journal = {Engineering Applications of Artificial Intelligence}, volume = {45}, number = {C}, pages = {269--280}, publisher = {Pergamon Press, Inc.}, address = {Tarrytown, NY, USA}, keywords = {ambient assisted living, cognitive robotics, Echo state networks, multivariate time-series, pervasive computing, planning, reservoir computing, robotic ecology, wireless sensor networks}, pubstate = {published}, tppubtype = {article} } |
Davide, Bacciu; J.G., Lisboa Paulo; Alessandro, Sperduti; Thomas, Villmann Probabilistic Modeling in Machine Learning Incollection Kacprzyk, Janusz ; Pedrycz, Witold (Ed.): pp. 545–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015, ISBN: 978-3-662-43505-2. Links | BibTeX | Tags: Bayesian networks, generative model, graphical models, hidden Markov models @incollection{Bacciu2015, title = {Probabilistic Modeling in Machine Learning}, author = {Bacciu Davide and Lisboa Paulo J.G. and Sperduti Alessandro and Villmann Thomas}, editor = {Kacprzyk, Janusz and Pedrycz, Witold}, url = {http://dx.doi.org/10.1007/978-3-662-43505-2_31}, doi = {10.1007/978-3-662-43505-2_31}, isbn = {978-3-662-43505-2}, year = {2015}, date = {2015-01-01}, pages = {545--575}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, keywords = {Bayesian networks, generative model, graphical models, hidden Markov models}, pubstate = {published}, tppubtype = {incollection} } |
Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli; Erina, Ferro; Luigi, Fortunati; Filippo, Palumbo; Oberdan, Parodi; Federico, Vozzi; Sten, Hanke; Johannes, Kropf; Karl, Kreiner Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9273 , Springer Verlag, 2015. Links | BibTeX | Tags: activity recognition, ambient assisted living, biomedical data, multivariate time-series, pervasive computing, wireless sensor networks @conference{11568_775269, title = {Smart environments and context-awareness for lifestyle management in a healthy active ageing framework}, author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Ferro Erina and Fortunati Luigi and Palumbo Filippo and Parodi Oberdan and Vozzi Federico and Hanke Sten and Kropf Johannes and Kreiner Karl}, url = {http://springerlink.com/content/0302-9743/copyright/2005/}, doi = {10.1007/978-3-319-23485-4_6}, year = {2015}, date = {2015-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {9273}, pages = {54--66}, publisher = {Springer Verlag}, keywords = {activity recognition, ambient assisted living, biomedical data, multivariate time-series, pervasive computing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Filippo, Benedetti; Alessio, Micheli ESNigma: efficient feature selection for Echo State Networks Conference Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), i6doc.com publ., 2015. Abstract | Links | BibTeX | Tags: Echo state networks, feature selection, multivariate time-series, pervasive computing, reservoir computing, structured data processing, wireless sensor networks @conference{11568_774434, title = {ESNigma: efficient feature selection for Echo State Networks}, author = {Bacciu Davide and Benedetti Filippo and Micheli Alessio}, url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-104.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15)}, pages = {189--194}, publisher = {i6doc.com publ.}, abstract = {The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as $60\%$ of the time required by the original wrapper.}, keywords = {Echo state networks, feature selection, multivariate time-series, pervasive computing, reservoir computing, structured data processing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as $60%$ of the time required by the original wrapper. |
Davide, Bacciu; Stefania, Gnesi; Laura, Semini Using a Machine Learning Approach to Implement and Evaluate Product Line Features Conference Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015, 188 , Electronic Proceedings in Theoretical Computer Science (EPTCS) 2015. Abstract | Links | BibTeX | Tags: big data, bike sharing system, kernel methods, software engineering, support vector machine @conference{11568_766969, title = {Using a Machine Learning Approach to Implement and Evaluate Product Line Features}, author = { Bacciu Davide and Gnesi Stefania and Semini Laura}, url = {http://dx.doi.org/10.4204/EPTCS.188.8}, doi = {10.4204/EPTCS.188.8}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015}, journal = {ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE}, volume = {188}, pages = {75--83}, series = {Electronic Proceedings in Theoretical Computer Science (EPTCS)}, abstract = {Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.}, keywords = {big data, bike sharing system, kernel methods, software engineering, support vector machine}, pubstate = {published}, tppubtype = {conference} } Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation. |
2014 |
Davide, Bacciu; Paolo, Barsocchi; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli An experimental characterization of reservoir computing in ambient assisted living applications Journal Article Neural Computing and Applications, 24 (6), pp. 1451-1464, 2014, ISSN: 0941-0643. Abstract | Links | BibTeX | Tags: ambient assisted living, Echo state networks, indoor user movement forecasting, reservoir computing, wireless sensor networks @article{nca2014, title = {An experimental characterization of reservoir computing in ambient assisted living applications}, author = {Bacciu Davide and Barsocchi Paolo and Chessa Stefano and Gallicchio Claudio and Micheli Alessio}, url = {http://dx.doi.org/10.1007/s00521-013-1364-4, Publisher version https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data, Dataset @ UCI}, doi = {10.1007/s00521-013-1364-4}, issn = {0941-0643}, year = {2014}, date = {2014-01-01}, journal = {Neural Computing and Applications}, volume = {24}, number = {6}, pages = {1451-1464}, publisher = {Springer London}, abstract = {In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.}, keywords = {ambient assisted living, Echo state networks, indoor user movement forecasting, reservoir computing, wireless sensor networks}, pubstate = {published}, tppubtype = {article} } In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks. |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Modeling Bi-directional Tree Contexts by Generative Transductions Conference Neural Information Processing, 8834 , Springer International Publishing, 2014. Abstract | Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, kernel methods, tree kernel, tree structured data @conference{11568_665864, title = {Modeling Bi-directional Tree Contexts by Generative Transductions}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro}, url = {http://dx.doi.org/10.1007/978-3-319-12637-1_68}, doi = {10.1007/978-3-319-12637-1_68}, year = {2014}, date = {2014-01-01}, booktitle = {Neural Information Processing}, journal = {LECTURE NOTES IN COMPUTER SCIENCE}, volume = {8834}, pages = {543--550}, publisher = {Springer International Publishing}, abstract = {We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks.}, keywords = {generative model, graphical models, hidden tree Markov model, kernel methods, tree kernel, tree structured data}, pubstate = {published}, tppubtype = {conference} } We introduce an approach to integrate bi-directional contexts in a generative tree model by means of structured transductions. We show how this can be efficiently realized as the composition of a top-down and a bottom-up generative model for trees, that are trained independently within a circular encoding-decoding scheme. The resulting input-driven generative model is shown to capture information concerning bi-directional contexts within its state-space. An experimental evaluation using the Jaccard generative kernel for trees is presented, indicating that the approach can achieve state of the art performance on tree classification benchmarks. |
Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Maurizio, Di Rocco; Alessandro, Saffiotti Learning context-aware mobile robot navigation in home environments Conference Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014), IEEE, 2014, ISBN: 9781479961702. Abstract | Links | BibTeX | Tags: ambient assisted living, Echo state networks, feature selection, multivariate time-series, pervasive computing, planning, recurrent neural network, reservoir computing, robotic ecology, wireless sensor networks @conference{11568_588269, title = {Learning context-aware mobile robot navigation in home environments}, author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Di Rocco Maurizio and Saffiotti Alessandro}, doi = {10.1109/IISA.2014.6878733}, isbn = {9781479961702}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014)}, pages = {57--62}, publisher = {IEEE}, abstract = {We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task}, keywords = {ambient assisted living, Echo state networks, feature selection, multivariate time-series, pervasive computing, planning, recurrent neural network, reservoir computing, robotic ecology, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Integrating bi-directional contexts in a generative kernel for trees Conference Neural Networks (IJCNN), 2014 International Joint Conference on, IEEE, 2014. Links | BibTeX | Tags: generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data, tree transductions @conference{11568_586070, title = {Integrating bi-directional contexts in a generative kernel for trees}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro}, doi = {10.1109/IJCNN.2014.6889768}, year = {2014}, date = {2014-01-01}, booktitle = {Neural Networks (IJCNN), 2014 International Joint Conference on}, pages = {4145--4151}, publisher = {IEEE}, keywords = {generative model, graphical models, hidden tree Markov model, kernel methods, structured data processing, tree kernel, tree structured data, tree transductions}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications Conference Communications in Computer and Information ScienceEngineering Applications of Neural Networks, 459 , Springer International Publishing, 2014. Abstract | Links | BibTeX | Tags: ambient assisted living, feature selection, multivariate time-series, pervasive computing, structured data processing, unsupervised learning, wireless sensor networks @conference{icfEann14, title = {An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications}, author = {Bacciu Davide}, doi = {10.1007/978-3-319-11071-4_4}, year = {2014}, date = {2014-01-01}, booktitle = {Communications in Computer and Information ScienceEngineering Applications of Neural Networks}, journal = {COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE}, volume = {459}, pages = {39--48}, publisher = {Springer International Publishing}, abstract = {The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.}, keywords = {ambient assisted living, feature selection, multivariate time-series, pervasive computing, structured data processing, unsupervised learning, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries. |
2013 |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model Journal Article Neural Networks and Learning Systems, IEEE Transactions on, 24 (2), pp. 231 -247, 2013, ISSN: 2162-237X. Links | BibTeX | Tags: generative topographic mapping, hidden Markov models, hidden tree Markov model, self-organizing map, tree structured data @article{gmtsdII2012, title = {Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6395856}, doi = {10.1109/TNNLS.2012.2228226}, issn = {2162-237X}, year = {2013}, date = {2013-02-01}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {24}, number = {2}, pages = {231 -247}, keywords = {generative topographic mapping, hidden Markov models, hidden tree Markov model, self-organizing map, tree structured data}, pubstate = {published}, tppubtype = {article} } |
Davide, Bacciu; A, Etchells Terence; JG, Lisboa Paulo; Joe, Whittaker Efficient identification of independence networks using mutual information Journal Article Computational Statistics, 28 (2), pp. 621-646, 2013, ISSN: 0943-4062. Links | BibTeX | Tags: Bayesian networks, graphical models, mutual information, PC algorithm @article{bgm2013, title = {Efficient identification of independence networks using mutual information}, author = {Bacciu Davide and Etchells Terence A and Lisboa Paulo JG and Whittaker Joe}, url = {http://dx.doi.org/10.1007/s00180-012-0320-6}, doi = {10.1007/s00180-012-0320-6}, issn = {0943-4062}, year = {2013}, date = {2013-01-01}, journal = {Computational Statistics}, volume = {28}, number = {2}, pages = {621-646}, publisher = {Springer-Verlag}, keywords = {Bayesian networks, graphical models, mutual information, PC algorithm}, pubstate = {published}, tppubtype = {article} } |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti An input–output hidden Markov model for tree transductions Journal Article Neurocomputing, 112 , pp. 34–46, 2013, ISSN: 0925-2312. Links | BibTeX | Tags: hidden Markov models, hidden tree Markov model, structured data processing, tree transductions @article{bacciuNeuroComp2013, title = {An input–output hidden Markov model for tree transductions}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro }, url = {http://www.sciencedirect.com/science/article/pii/S0925231213001914}, doi = {10.1016/j.neucom.2012.12.044}, issn = {0925-2312}, year = {2013}, date = {2013-01-01}, journal = {Neurocomputing}, volume = {112}, pages = {34--46}, keywords = {hidden Markov models, hidden tree Markov model, structured data processing, tree transductions}, pubstate = {published}, tppubtype = {article} } |
Nicola, Di Mauro; Paolo, Frasconi; Fabrizio, Angiulli; Davide, Bacciu; de Marco, Gemmis; Floriana, Esposito; Nicola, Fanizzi; Stefano, Ferilli; Marco, Gori; A, Lisi Francesca; others, Italian Machine Learning and Data Mining research: The last years Journal Article Intelligenza Artificiale, 7 (2), pp. 77–89, 2013. Links | BibTeX | Tags: graphical models, recurrent neural network, structured data processing @article{di2013italian, title = {Italian Machine Learning and Data Mining research: The last years}, author = {Di Mauro Nicola and Frasconi Paolo and Angiulli Fabrizio and Bacciu Davide and de Gemmis Marco and Esposito Floriana and Fanizzi Nicola and Ferilli Stefano and Gori Marco and Lisi Francesca A and others}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263}, doi = {10.3233/IA-130050}, year = {2013}, date = {2013-01-01}, journal = {Intelligenza Artificiale}, volume = {7}, number = {2}, pages = {77--89}, publisher = {IOS Press}, keywords = {graphical models, recurrent neural network, structured data processing}, pubstate = {published}, tppubtype = {article} } |
Davide, Bacciu; Claudio, Gallicchio; Alessandro, Lenzi; Stefano, Chessa; Alessio, Micheli; Susanna, Pelagatti; Claudio, Vairo Distributed Neural Computation over WSN in Ambient Intelligence Conference Advances in Intelligent Systems and Computing - Ambient Intelligence - Software and Applications, 219 , Springer Verlag, 2013. Links | BibTeX | Tags: ambient assisted living, Echo state networks, multivariate time-series, pervasive computing, reservoir computing, wireless sensor networks @conference{11568_238038, title = {Distributed Neural Computation over WSN in Ambient Intelligence}, author = {Bacciu Davide and Gallicchio Claudio and Lenzi Alessandro and Chessa Stefano and Micheli Alessio and Pelagatti Susanna and Vairo Claudio }, doi = {10.1007/978-3-319-00566-9_19}, year = {2013}, date = {2013-01-01}, booktitle = {Advances in Intelligent Systems and Computing - Ambient Intelligence - Software and Applications}, journal = {ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING}, volume = {219}, pages = {147--154}, publisher = {Springer Verlag}, keywords = {ambient assisted living, Echo state networks, multivariate time-series, pervasive computing, reservoir computing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } |
Davide, Bacciu; Stefano, CHESSA; Claudio, Gallicchio; Alessio, MICHELI; Paolo, Barsocchi An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living Conference Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets, 19 , Springer, 2013. Links | BibTeX | Tags: Echo state networks, indoor user movement forecasting, pervasive computing, recurrent neural network, reservoir computing, structured data processing, wireless sensor networks @conference{11568_159900, title = {An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living}, author = {Bacciu Davide and CHESSA Stefano and Gallicchio Claudio and MICHELI Alessio and Barsocchi Paolo}, doi = {10.1007/978-3-642-35467-0_5}, year = {2013}, date = {2013-01-01}, booktitle = {Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets}, journal = {SMART INNOVATION, SYSTEMS AND TECHNOLOGIES}, volume = {19}, pages = {41--50}, publisher = {Springer}, keywords = {Echo state networks, indoor user movement forecasting, pervasive computing, recurrent neural network, reservoir computing, structured data processing, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } |
2012 |
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees Journal Article Neural Networks and Learning Systems, IEEE Transactions on, 23 (12), pp. 1987 -2002, 2012, ISSN: 2162-237X. Links | BibTeX | Tags: hidden Markov models, hidden tree Markov model, tree structured data @article{gmtsdI2012, title = {Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees}, author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6353263}, doi = {10.1109/TNNLS.2012.2222044}, issn = {2162-237X}, year = {2012}, date = {2012-12-01}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {23}, number = {12}, pages = {1987 -2002}, keywords = {hidden Markov models, hidden tree Markov model, tree structured data}, pubstate = {published}, tppubtype = {article} } |
Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessandro, Lenzi; Alessio, Micheli; Susanna, Pelagatti A General Purpose Distributed Learning Model for Robotic Ecologies Conference Robot Control - 10th IFAC Symposium on Robot Control, 10 , ELSEVIER SCIENCE BV, 2012. Abstract | Links | BibTeX | Tags: distributed learning, multivariate time-series, pervasive computing, recurrent neural network, reservoir computing, robotic ecology, wireless sensor networks @conference{11568_193770, title = {A General Purpose Distributed Learning Model for Robotic Ecologies}, author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Lenzi Alessandro and Micheli Alessio and Pelagatti Susanna}, url = {http://www.ifac-papersonline.net/Detailed/55807.html}, doi = {10.3182/20120905-3-HR-2030.00178}, year = {2012}, date = {2012-01-01}, booktitle = {Robot Control - 10th IFAC Symposium on Robot Control}, journal = {IFAC PROCEEDINGS VOLUMES}, volume = {10}, pages = {435--440}, publisher = {ELSEVIER SCIENCE BV}, abstract = {The design of a learning system for robotic ecologies need to account for some key aspects of the ecology model such as distributivity, heterogeneity of the computational, sensory and actuator capabilities, as well as self-configurability. The paper proposes general guiding principles for learning systems' design that ensue from key ecology properties, and presents a distributed learning system for the Rubicon ecology that draws inspiration from such guidelines. The proposed learning system provides the Rubicon ecology with a set of general-purpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams.}, keywords = {distributed learning, multivariate time-series, pervasive computing, recurrent neural network, reservoir computing, robotic ecology, wireless sensor networks}, pubstate = {published}, tppubtype = {conference} } The design of a learning system for robotic ecologies need to account for some key aspects of the ecology model such as distributivity, heterogeneity of the computational, sensory and actuator capabilities, as well as self-configurability. The paper proposes general guiding principles for learning systems' design that ensue from key ecology properties, and presents a distributed learning system for the Rubicon ecology that draws inspiration from such guidelines. The proposed learning system provides the Rubicon ecology with a set of general-purpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams. |
G, Lisboa Paulo J; H, Jarman Ian; A, Etchells Terence; J, Chambers Simon; Davide, Bacciu; Joe, Whittaker; M, Garibaldi Jon; Sandra, Ortega-Martorell; Alfredo, Vellido; O, Ellis Ian Discovering Hidden Pathways in Bioinformatics Conference Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics, 7548 , 2012. Abstract | Links | BibTeX | Tags: Bayesian networks, biomedical data, graphical models, mutual information, PC algorithm @conference{11568_465481, title = {Discovering Hidden Pathways in Bioinformatics}, author = {Lisboa Paulo J G and Jarman Ian H and Etchells Terence A and Chambers Simon J and Bacciu Davide and Whittaker Joe and Garibaldi Jon M and Ortega-Martorell Sandra and Vellido Alfredo and Ellis Ian O}, doi = {10.1007/978-3-642-35686-5_5}, year = {2012}, date = {2012-01-01}, booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics}, journal = {LECTURE NOTES IN COMPUTER SCIENCE}, volume = {7548}, pages = {49--60}, abstract = {The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076)}, keywords = {Bayesian networks, biomedical data, graphical models, mutual information, PC algorithm}, pubstate = {published}, tppubtype = {conference} } The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076) |
Same, Abdel-Naby; Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Roberto, Guzman; Raul, Lopez; Hector, Lozano; Liam, Maguire; Martin, McGinnity T; Alessio, Micheli; MP, O'Hare Greg; Federico, Pecora; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo Robotic UBIquitous COgnitive Networks Presentation 01.01.2012. BibTeX | Tags: cognitive robotics, pervasive computing, planning, robotic ecology @misc{11568_466873, title = {Robotic UBIquitous COgnitive Networks}, author = {Abdel-Naby Same and Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano Hector and Maguire Liam and McGinnity T Martin and Micheli Alessio and O'Hare Greg MP and Pecora Federico and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio}, year = {2012}, date = {2012-01-01}, booktitle = {Poster in the 5th International Conference on Cognitive Systems (CogSys 2012)}, keywords = {cognitive robotics, pervasive computing, planning, robotic ecology}, pubstate = {published}, tppubtype = {presentation} } |
