5th SmartData@PoliTO Workshop (Internal Only)

The Fifth Workshop for the Interdepartmental Center SmartData@PoliTO will be held on September 26th and 27th, 2019 in Langhe (Piedmont).

Date: Thursday September 26th and Friday September 27th
Location: Hotel Barolo, Via Lomondo, 2, 12060 Barolo (CN)

Chairs: Daniele Apiletti & Martino Trevisan

Thursday, September 26th

Coffee break: 10:00 – 10:30

Session 1: 10:30 – 12:30

Marco Mellia: Introduction
Gianluca Setti: TBD
PhD presentations:
  • Marco Guerra:  Principled Homological Scaffold for the Brain Functional Connectome via Minimal Bases
  • Antonio Mastropietro and Alessandro De Gregorio: Interpretability of a Neural Network classifier with SHAP and Topological Data Analysis for psilocybin effect on human brain
  • Francesco Della Santa: Discontinuous Neural Networks
  • Ulderico Fugacci: Topology-Based Tools for Data Classification

Lunch: 13:00 – 14:00

Session 2: 14:00 – 15:30:

Pietro Michiardi: TBD
PhD presentations:
  • Francesco Ventura: Explaining Black-Box models for unstructured data
  • Mirko Pieropan: Expectation Propagation for the diluted bayesian perceptron classifier
Michela Meo: Greener network operation through machine learning

The talk focuses on the support that machine learning can provide to green  network operation. Green networks combine resource and energy management decisions; where resource management includes the use of sleep modes and resource on demand decisions and energy management involves the choice of which energy source to use, in a power supply system that integrates renewable energy generation into a traditional supply.  The case of a radio access network is made and the impacts of different traffic prediction models on the consumed energy mix and on QoS is evaluated. The results show that a widespread implementation of energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

Degustazione: 16:00 – 17:00

Session 3: 17:30 – 19:00

SmartData collaborations with enterprises:
  • Dena Markudova: Activities with Tierra S.p.A
  • Tania Cerquitelli: TBD
  • Luca Vassio: How to design an electric free floating car sharing service?
  • Daniela Renga: Transparently mining data from a medium-voltage distribution network: a prognostic-diagnostic analysis
  • Fabrizio Lamberti: Trasformazione digitale nel settore assicurativo: Casi di studio di Reale Group
  • Danilo Giordano: Anatomy of a Predictive Maintenance Pipeline: A Powertrain use case
PhD school experiences

Dinner: 20:00 – 21:30

Friday, September 27th

Breakfast: 8:00 – 9:00

Session 4: 9:00 – 10:30

Mauro Gasparini e Lidia Sacchetto: Model-based binary classification based on binary data

I will review model based classification, both with hard and soft assignment of the units, in the case of binary features (predictors). I will explore the properties of the ROC curve in this special case and provide some examples. This is joint work with my Ph.d. student Lidia Sacchetto.

PhD presentations:
  • Andrea Pasini: A Data Science Pipeline for Automatic Issue-Detection in Engines
  • Eliana Pastor: A Density-based Preprocessing Technique to Scale Out Clustering
  • Marilisa Montemurro: Silhouette score to assess biological sample dissimilarity
  • Moreno La Quatra: Using Regression Models to Pinpoint Relevant Content in Research Papers
  • Elena Daraio: Characterizing air-quality data through unsupervised analytics methods

Coffee break: 10:30 – 11:00

Session 5: 11:00 – 12:45

Sandra Di Rocco: TBD
PhD presentations:
  • Andrea Morichetta: Unsupervised learning for network traffic analysis
  • Michele Cocca: Free Floating carsharing: from combustion engine to electric vehicles
Alberto Pisoni: TBD
PhD presentations:
  • Thomas Favale: Privacy compliant network monitoring probe
  • Flavio Giobergia: Mining Sensor Data for Predictive Maintenance in the Automotive Industry
  • Vittorio Mazzia: Improvement in land cover and crop classification based on CNN in combination with RNN

Lunch: 13:00 – 14:00

Session 5: 14:00 – 16:00

Andrea Calimera: ConvNet on Tiny Cores

The promise of the IoT is to improve the quality of services using the information inferred from data collected across distributed platforms. The next generation of smart-objects will be able to distill such information at the edge, where data are generated, by-passing (or at least limiting) the access to the cloud. The success of this strategy involves the deployment of complex data-analytics algorithms, like deep neural models, on low-power devices. The objective of this talk is to introduce practical optimization techniques aimed at improving the energy efficiency of deep neural networks made run on tiny embedded computers.

PhD presentations:
  • Andrea Bordone Molini: Deep neural network for Super-resolution of Unregistered Multitemporal images
  • Giuseppe Attanasio: Quantitative cryptocurrency trading: exploring the use of machine learning techniques
  • Alessandro Destefanis: The impact of Airbnb on the hotels’ economic perfomance
  • Nicola Prette: Video Compression using Neural Networks
  • Sina Famouri: Towards robust training of Faster RCNN for breast mass detection and classification
Marco Mellia: Final Remarks

Coffee break: 16:00 – 16:30