MIND

1. MIND Models indecision making & data @nalysis Enza Messina and Francesco Archetti

2. Main Activities • Research Areas • Machine Learning Algorithms • Probabilistic and Relational Models • Optimization Under Uncertainty • Multimedia Document • Life Sciences • Ambient Intelligence • Finance • Applicative Domains Faculty: Francesco Archetti Enza Messina Guglielmo Lulli Post Doc: Elisabetta Fersini PhD: Federica Bargna Others: Daniele Toscani Ilaria Giordani Gaia Arosio Luigi Quarenghi

3. Machine Learning and Relational Data • Traditional learning methods are consistent with the classical statistical inference problem formulation • istances are independent and identically distributed (i.i.d.) • but do not reflect the real world! • We need a solution able to deal with relationships and with uncertainty in more general terms ProbabilisticModels LearningTechniques ProbabilisticModels LearningTechniques SL SRL Relational Representation

4. The World is Uncertain Graphical Models (here e.g. a Bayesian network) - model uncertainty explicitly by representing the joint distribution Fever Ache Random Variables Influenza Direct Influences Propositional Model!

5. Real-World Data are structured Non- i.i.d PatientID Date Physician Symptoms Diagnosis P1 1/1/01 Smith palpitations hypoglycemic P1 2/1/03 Jones fever, aches influenza PatientID Gender Birthdate P1 M 3/22/63 First-Order Logic / Relational Databases PatientID Date Lab Test Result PatientID SNP1 SNP2 … SNP500K P1 AA AB BB P2 AB BB AA P1 1/1/01 blood glucose 42 P1 1/9/01 blood glucose 45 PatientID Date Prescribed Date Filled Physician Medication Dose Duration P1 5/17/98 5/18/98 Jones prilosec 10mg 3 months

6. Heterogeneous Information LEARNER Inference Gene Cluster Exp. type GCN4 HSF Lipid Exp. cluster Endoplasmatic Level Probabilistic Relational Models • Integrate uncertainty with relational model • Convenient language for specifying complex models • “Web of influence”: subtle & intuitive reasoning • Framework for incorporating heterogeneous data by connecting related entities (consider also relation uncertainty) • New problems: • Relational clustering • Collective classification • Open Problems: Inference and Learning

7. Uncertainty, Relations, Dynamics DPRM PRM,RBN,SLP… Bayes Net DBN Causal Relationships MRDM, ILP Struct. Rel Relational Markov Model Sequence (Hidden) Markov Model

8. Some Applications

9. #origin_ref #destination_ref ♦ document_id class Document Analysis • Learning Models for Relational Data: • Relational Clustering 1. ConstraintLearning 2. ObjectiveFunctionAdaptation • Relational Classification: • Probabilistic Relational Models with Relational Uncertainty • Conditional Random Fields Publications E. Fersini, E. Messina, F. Archetti, “A probabilistic relational approach for web document clustering”, Journal of Information Processing and Management, Vol. 46, no 2, p. 117-130, 2010. E. Fersini, E. Messina, F. Archetti. “Web page classification: A probabilistic model with relational uncertainty”. In Proc. of the 2010 Conference on Information Processing and Management of Uncertainty, 2010. E. Fersini, E. Messina, F. Archetti, Probabilistic relational models with relational uncertainty: an early study in web page classification, IEEE WI-IAT Workshop, 2009. Submitted E. Fersini, E. Messina, F. Archetti, “Probabilistic relational models with relational uncertainty”, Journal of Information Processing and Management, (second revision).

10. Document AnalysisE-Forensics • InformationExtraction • Hearing Summarization • EmotionRecognition JUdicial MAnagement by Digital Libraries Semantics

11. Document AnalysisE-Forensics Publications E. Fersini, E. Messina, F. Archetti. “Multimedia Summarization in Law Courts: A Clustering-based Environment for Browsing and Consulting Judicial Folders”. In proc. of the 10th Industrial Conference on Data Mining, 2010. E. Fersini, G. Arosio, E. Messina, F. Archetti, “Emotion recognition in judicial domain: a multilayer SVM approach, LNAI, in Proc. of the 6th International Conference on Machine Learning and Data Mining, Leipzig, 2009. E. Fersini, G. Arosio, E. Messina, F. Archetti, D. Toscani. Multimedia Summarization in Law Courts: An Environment for Browsing and Consulting Judicial Folders. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009. E. Fersini, F. Callegaro, M. Cislaghi, R. Mazzilli, S. Somaschini, R. Muscillo, D. Pellegrini,. Managing Knowledge Extraction and Retrieval from Multimedia Contents: a Case Study in Judicial Domain. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009. G. Felici, E. Fersini, E. Messina, Information extraction through constrained inference in Conditional Random Fields, AIRO 2010, september 2010. Submitted E. Fersini, E. Messina, F. Archetti. “Emotional States in Judicial Courtrooms: An Experimental Investigation”. Sumbitted to Journal of Speech Commiunication. E. Fersini, E. Messina, D. Toscani, F. Archetti, M. Cislaghi. Semantics and machine learning for building the next generation of judicial case and court management systems. Submitted to the Int. Conference on Knowledge Management and Information Sharing Submitted Projects • Progetto PON • eJRM - electronic Justice Relationship Management

12. Life Sciences

13. Systems Biology Applications Learning gene regulatory networks Gene DNA Human cancer Control Coding + Transcription TF Gene expression Drug Activity RNA single strand Regulatory modules Modelling the pharmacology of cancer Gene drug interaction identification of a drug treatment for a given cell line based both on drug activity pattern and gene expression profile Collaborations

14. Pharmacogenomics Application: Predict drug response to oral anticoagulation therapy (OAT) Grouping (Profiling) patients based on their clinical and genotypic features in order to suggest doctors the correct drug dosage Haemorragic risk • Data of about 4000 patients: • Clinical and therapeutical data: personal patients data, medical diagnosis, therapy, INR and dosage measurements • Genetic data: polymorphism of three genes: CYP2C9, VKORC1 and CYP4F2 that contribute to differences in patients’ response. Thrombotic risk In collaboration with 14

15. Publications • G. Ogliari, I. Giordani, A. Mihalich, D. Castaldi, A. Di Blasio, A. Dubini, E. Messina, F. Archetti, D. Mari, Nuova classificazione clinica e Farmacogenetica per predire la dinamica dell'inr nell'anziano in tao. Giornale di gerontologia, vol. lvii; p. 495-496, issn: 0017-0305, dicembre 2009 • F. Archetti, I. Giordani, E. Messina, G. Ogliari, D. Mari, "A comparison of data mining approaches in the categorization of oral anticoagulant patients", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009 • E. Fersini, C. Manfredotti, E. Messina, F. Archetti Relational K-Means for Gene Expression Profiles and Drug Activity Pattern Analysis, to appear on Int. Journal of Mathematical Modelling and Algorithms. • F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for Anticancer Therapeutic Response Prediction using the NCI-60 Dataset”, Computers & Operations Research, Vol.37, No.8, pp.1395-1405, August 2010. • E. Fersini, I.Giordani, E.Messina, F. Archetti, "Relational Clustering and Bayesian Networks for Linking Gene Expression Profiles and Drug Activity Patterns", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009. • L. Vanneschi , F. Archetti, M. Castelli, I. Giordani, "Classification of Oncologic Data with Genetic Programming," Journal of Artificial Evolution and Applications, vol. 2009, Article ID 848532, 13 pages, 2009. doi:10.1155/2009/848532. • F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for QSAR Investigation of Docking Energy”, Applied Soft Computing, Vol. 10, No. 1, pp. 170-182, issn: 1568-4946, Jan 2010. Submitted F. Archetti, I.Giordani, G.Mauri, E.Messina. “A new clustering approach for learning transcriptional regulatory modules”, submitted to Int. Journal of Data Mining and Bioinformatics, (second revision).

16. Projects • Submitted proposals: • Associazione lotta alla trombosi - Call for applications 2010 • Oral Anticoagulation Therapy in the elderly and women • Partners: • Brunel University, Centre for Intelligent Data Analysis • Harvard Medical School, Biomedical Cybernetics Laboratory • Univ. of Milano, Dept. of Medical Sciences, Geriatrics Unit • Ist. Clinico Humanitas - Thrombosis Unit (Corrado Lodigiani, MD, PhD) • Ist. Auxologico Italiano, IRCCS Centro di Ricerche e Tecnologie Biomediche, • PON • HEARTDRIVE • Project Coordinator: Calpark – Parco Tecnologico e Scientifico della Calabria • PRIN • Revealing common patterns among insuline resistance, osteoporosis and chronic inflammatory diseases by using Bayesian Networks. • Project Coordinator: Università degli Studi "Magna Graecia" di CATANZARO

17. Ambient Intelligence

18. Multi-target tracking Multi-target tracking: finding the tracks of an unknown number of moving targets from noisy observations. • Exploiting relations can improve the efficiency of the tracker • Monitoring relations can be a goal in itself We model the transition probability of the system with a RDBN. • A new representation modelling not only objects but also their relations • A new computational strategy based on a family of Sequential Monte Carlo methods called Particle Filter • Statistical techniques for the detection of anomalous behaviours Publications • Cristina E. Manfredotti, Enza Messina: Relational Dynamic Bayesian Networks to Improve Multi-target Tracking. ACIVS 2009: 528-539. • C. Manfredotti, E. Messina, D.J. Fleet, Relations to improve multi-target tracking in an activity recognition system.Proceedings of the International Conference on Imaging for Crime Detection and Prevention, London, 2009. In collaboration with

19. Wireless Sensor Networks CH5 sink CH2 CH1 CH4 BN CH3 WSN Publications F. Archetti, E. Messina, D. Toscani and M. Frigerio - IKNOS – Inference and Knowledge in Networks Of Sensors. International Journal of Sensor Networks (IJSNet), Vol.8 No. 3, 2010 F. Chiti, R. Fantacci, F. Archetti, E. Messina, D. Toscani, Integrated Communications Framework for Context aware Continuous Monitoring with Body Sensor Networks,IEEE Journal on Selected Areas in Communications - Wireless and Pervasive Communications for Healthcare.Volume 27, Issue 4, 2009. D. Toscani, I. Giordani, M. Cislaghi, L. Quarenghi. Querying Sensor Data for Environmental Monitoring. Submitted to International Journal of Sensor Networks (IJSNet), 2010 D. Toscani, I. Giordani, L. Quarenghi, F. Archetti . A software Environment For Supporting Sensor Querying. Submitted to IEEE Sensors 2010 Conference, Hawaii, 2010 Submitted • Bayesian abstractions for virtual sensing through low cost data aggregation and net-wide anomaly detection • Modelling Cluster Heads as nodes of a BN • Inference to know sensor values also in presence of temporary faults: • Lack of communication (sensor failure or sleep) • Outlier due to sensor malfunctioning 19

20. Transportation & Logistics Lu f Pj f v h j destf u origf w k Data Models Decisions In collaboration with: PRIN MIUR Enhancing the European Air Transportation System Partners: Università di Padova, Università di Trieste. Projects

21. Ambient Intelligence Projects LENVIS - Localised environmental and health information services for all (EU-FP7) sviluppo di una rete collaborativa di supporto alle decisioni, per lo scambio di informazioni e servizi riguardanti l'ambiente e la salute Publications D. Toscani, L. Quarenghi, F.Bargna, F. Archetti, E. Messina, "A DSS for Assessing the Impact of Environmental Quality on Emergency Hospital Admissions", In proceedings of the WHCM 2010 - IEEE Workshop on Health Care Management, February 18-20, 2010 - Venice, Italy. Submitted D. Toscani, I. Giordani, F. Bargna, L. Quarenghi, F. Archetti. A software System for Data Integration and Decision Support for Evaluation of Air Pollution Health Impact. Submitted to ICEIS 2010 - 12th International Conference on Enterprise Information Systems. Funchal, Madeira – Portugal, 2010

22. Projects In collaboration with SAL Lab. INSYEME – Integrated Systems for Emergencies (MIUR - FIRB) GREIS - Gestione del Risparmio Energetico attraverso Informazioni di Sicurezza (MIUR) In collaboration withNOMADIS Lab. H-CIM Health Care through Intelligent Monitoring (MIUR) Submitted FP7 ICT call 6 - STREP OPENCITY Open framework for Transport Demand Management for smart and sustainable urban mobility in an open and accessible city Project Coordinator: Consorzio Milano Ricerche In collaboration with SAL Lab. e Imaging & Vision Lab. FLECS – FLy’s eyes for Collaborative Surveillance – (Progetto PON)

23. Financial Time Series

24. Financial Time Series & Scenario Generation • Regime Switching Models • Observations: prices • Hidden var.: Regime Transition Model Markov Chain Observation Model Mixture of Gaussians (Autoregressive Process) (Autoregressive) Hidden Markov Model 24

25. Financial Time Series • Extend state space models to more general Relational Dynamic Bayesian Networks to account not only prices but also, through CPT, “exogenous” economic factors and unstructured information • Algorithms for managing risk tracking portfolio using all available evidence and taking into account all uncertainties “Markets are good at gathering information from many heterogeneous sources and combining it appropriately, the same we would expect from models” Publications G. Consigli, C. Manfredotti, E. Messina, A sequential learning method for tracking stochastic volatility, EURO XXIV, July 2010, Lisbon  Projects & Collaborations PRIN 2007 "Modelli probabilistici per la rappresentazione dell’incertezza per la definizione di metodologie di selezione del portafoglio” (Università di Bergamo, Università della Calabria) Collaboration with Brunel University and CARISMA Research Centre: Workshop “Application of Hidden Markov Models and Filters to Time Series Methods in Finance”, London, September 2010

26. The cooperation network CARISMA Research Center Norwegian University of Science and Technology Brunel University University of Toronto SB RAS Russia Aachen University Hungarian Academy of Sciences Massachusset Institute of Technology Harvard Medical School Centre of Research and Technology Hellas