@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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},

urldate = {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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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},

urldate = {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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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},

urldate = {2014-01-01},

booktitle = {Neural Networks (IJCNN), 2014 International Joint Conference on},

pages = {4145--4151},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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},

urldate = {2014-01-01},

booktitle = {Communications in Computer and Information Science - Engineering 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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@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 = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466867,

title = {Self-Sustaining Learning for Robotic Ecologies},

author = {BACCIU Davide and Broxvall Mathias and Coleman Sonya and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano-Peiteado Hector and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012},

pages = {99--103},

abstract = {The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_156516,

title = {A Generative Multiset Kernel for Structured Data},

author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},

doi = {10.1007/978-3-642-33269-2_8},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {7552},

pages = {57--64},

publisher = {Springer-Verlag},

address = {BERLIN HEIDELBERG},

abstract = {The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_152836,

title = {Input-Output Hidden Markov Models for Trees},

author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

booktitle = {ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings},

pages = {25--30},

publisher = {Ciaco scrl - i6doc.com},

abstract = {The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_202140,

title = {Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Barsocchi Paolo and Chessa Stefano},

url = {http://ijcai-11.iiia.csic.es/files/proceedings/Space,%20Time%20and%20Ambient%20Intelligence%20Proceeding.pdf},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI)},

pages = {1--6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_145907,

title = {Adaptive Tree Kernel by Multinomial Generative Topographic Mapping},

author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},

url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6033423&contentType=Conference+Publications&refinements%3D4294413850%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6033131%29},

doi = {10.1109/IJCNN.2011.6033423},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the International Joint Conference on Neural Networks},

pages = {1651--1658},

publisher = {IEEE},

address = {Piscataway (NJ)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465483,

title = {Different Methodologies for Patient Stratification Using Survival Data},

author = {Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Lisboa Paulo JG},

doi = {10.1007/978-3-642-14571-1_21},

year  = {2010},

date = {2010-01-01},

booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {6160},

pages = {276--290},

abstract = {Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465482,

title = {Adaptive fuzzy-valued service selection},

author = {Bacciu Davide and Buscemi Maria Grazia and Mkrtchyan Lusine },

doi = {10.1145/1774088.1774598},

year  = {2010},

date = {2010-01-01},

booktitle = {Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10},

journal = {PROCEEDINGS OF THE .. ACM SYMPOSIUM ON APPLIED COMPUTING},

pages = {2467--2471},

abstract = {Service composition concerns both integration of heterogeneous distributed applications and dynamic selection of services. QoS-aware selection enables a service requester with certain QoS requirements to classify services according to their QoS guarantees. In this paper we present a method that allows for a fuzzy-valued description of QoS parameters. Fuzzy sets are suited to specify both the QoS preferences raised by a service requester such as 'response time must be as lower as possible and cannot be more that 1000ms' and approximate estimates a provider can make on the QoS capabilities of its services like 'availability is roughly between 95% and 99%'. We propose a matchmaking procedure based on a fuzzy-valued similarity measure that, given the specifications of QoS parameters of the requester and the providers, selects the most appropriate service among several functionally-equivalent ones. We also devise a method for dynamical update of service offers by means of runtime monitoring of the actual QoS performance.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_136433,

title = {Compositional Generative Mapping of Structured Data},

author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},

doi = {10.1109/IJCNN.2010.5596606},

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

booktitle = {Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10)},

pages = {1359--1366},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_142187,

title = {Bottom-Up Generative Modeling of Tree-Structured Data},

author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},

doi = {10.1007/978-3-642-17537-4_80},

year  = {2010},

date = {2010-01-01},

booktitle = {LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {6443},

pages = {660--668},

publisher = {Springer-Verlag},

address = {BERLIN HEIDELBERG},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466869,

title = {Model-based and model-free clustering: a case study of protein expression data for breast cancer},

author = {Lisboa Paulo JG and Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M},

year  = {2009},

date = {2009-01-01},

booktitle = {PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465485,

title = {p-Health in Breast Oncology: A Framework for Predictive and Participatory e-Systems},

author = { Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Paulo J G Lisboa},

doi = {10.1109/DeSE.2009.68},

year  = {2009},

date = {2009-01-01},

booktitle = {2009 Second International Conference on Developments in eSystems Engineering},

pages = {123--129},

publisher = {IEEE},

abstract = {Maintaining the financial sustainability of healthcare provision makes developments in e-Systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-Systems at the core of healthcare delivery, in contrast to current practice. This view of the demands of the 21st century sets an agenda that builds upon advances in engineering devices and computing infrastructure, but also computational intelligence and new models for communication between healthcare providers and the public. This paper gives an overview of p-Health with reference to decision support in breast cancer.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465484,

title = {Patient stratification with competing risks by multivariate Fisher distance},

author = {Bacciu Davide and Jarman Ian H and Etchells Terence A and Lisboa Paulo J G},

doi = {10.1109/IJCNN.2009.5179077},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

booktitle = {2009 International Joint Conference on Neural Networks},

pages = {3453--3460},

publisher = {IEEE},

abstract = {Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466669,

title = {Fuzzy Admission Control with Similarity Evaluation for VoWLAN with QoS Support},

author = {Bacciu Davide and Botta Alessio and Badia Leonardo },

doi = {10.1109/WONS.2008.4459355},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 Fifth Annual Conference on Wireless on Demand Network Systems and Services},

pages = {57--64},

publisher = {IEEE},

abstract = {In this paper, we make use of a fuzzy approach to determine a soft Admission Control mechanism for Voice-over-Internet-Protocol services over Wireless Local Area Network. In such a system, complicated interactions between service provider and clients take place, since the network capacity constraints must be matched with users' preferences and needs. Most of the difficulties in dealing with these interactions stem from the fact that it is very difficult to define both the load condition of the network and the users' requirements in a crisp manner. To this end, we define a framework in which the provider expresses the network status and the clients describe their preferences by means of an approach based on Fuzzy Set Theory. In this way, we are able to develop an Admission Control strategy, based on Similarity Evaluation techniques, that enforces the soft constraints expressed by the two parties. The obtained framework is numerically evaluated, showing the benefit of employing Fuzzy Set Theory with respect to the traditional crisp approach.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466667,

title = {Convergence Behavior of Competitive Repetition-Suppression Clustering},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1007/978-3-540-69158-7_52},

year  = {2008},

date = {2008-01-01},

booktitle = {Neural Information Processing, Lecture Notes in Computer Science},

volume = {4984},

pages = {497--506},

publisher = {Springer},

abstract = {Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_465487,

title = {Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics},

author = {BACCIU Davide and BIGANZOLI Elia and LISBOA Paulo JG and Starita Antonina},

doi = {10.1007/978-3-540-85565-1-23},

year  = {2008},

date = {2008-01-01},

booktitle = {Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08)},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {5178},

pages = {181--188},

publisher = {Springer},

abstract = {A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466674,

title = {A Fuzzy Approach for Negotiating Quality of Services},

author = {Bacciu Davide and Botta Alessio and Melgratti Hernan },

doi = {10.1007/978-3-540-75336-0_13},

year  = {2007},

date = {2007-01-01},

booktitle = {TRUSTWORTHY GLOBAL COMPUTING, Lecture Notes in Computer Science},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {4661},

pages = {200--217},

publisher = {Springer Verlag},

abstract = {A central point when integrating services concerns to the description, agreement and enforcement of the quality aspect of service interaction, usually known as Service Level Agreement (SLA). This paper presents a framework for SLA negotiation based on fuzzy sets. We propose (i) a request language for clients to describe quality preferences, (ii) a publication language for providers to define the qualities of their offered services, and (iii) a decision procedure for granting any client request with a SLA contract fitting the requestor requirements. We start with a restricted framework in which the different qualities of a service are handled independently (as being orthogonal) and then we propose an extension that allows clients and providers to express dependencies among different qualities.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}