University of Bologna

research

∙ 05/27/2022

Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers

Random Forests (RFs) are widely used Machine Learning models in low-powe...

Francesco Daghero, et al. ∙

research

∙ 07/27/2021

Towards Industrial Private AI: A two-tier framework for data and model security

With the advances in 5G and IoT devices, the industries are vastly adopt...

Sunder Ali Khowaja, et al. ∙

research

∙ 10/11/2019

A sub-Riemannian model of the visual cortex with frequency and phase

In this paper we present a novel model of the primary visual cortex (V1)...

E. Baspinar, et al. ∙

research

∙ 10/03/2019

A Commentary on "Breaking Row and Column Symmetries in Matrix Models"

The CP 2002 paper entitled "Breaking Row and Column Symmetries in Matrix...

Alan M. Frisch, et al. ∙

research

∙ 04/26/2021

A dissemination workshop for introducing young Italian students to NLP

We describe and make available the game-based material developed for a l...

Lucio Messina, et al. ∙

research

∙ 03/24/2022

Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devi...

Alessio Burrello, et al. ∙

research

∙ 05/13/2020

On the uncertainty of self-supervised monocular depth estimation

Self-supervised paradigms for monocular depth estimation are very appeal...

Matteo Poggi, et al. ∙

research

∙ 03/28/2022

Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks

Temporal Convolutional Networks (TCNs) are promising Deep Learning model...

Matteo Risso, et al. ∙

research

∙ 03/04/2022

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Modern real-time Structural Health Monitoring systems can generate a con...

Amirhossein Moallemi, et al. ∙

research

∙ 10/11/2019

Shooting Labels: 3D Semantic Labeling by Virtual Reality

Availability of a few, large-size, annotated datasets, like ImageNet, Pa...

Pierluigi Zama Ramirez, et al. ∙

research

∙ 04/26/2021

Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

Although Natural Language Processing (NLP) is at the core of many tools ...

Ludovica Pannitto, et al. ∙

research

∙ 09/16/2021

Reinforcement Learning on Encrypted Data

The growing number of applications of Reinforcement Learning (RL) in rea...

Alberto Jesu, et al. ∙

research

∙ 10/02/2021

Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

We propose a new method for generating explanations with Artificial Inte...

Francesco Sovrano, et al. ∙

research

∙ 02/01/2022

Improving Parametric Neural Networks for High-Energy Physics (and Beyond)

Signal-background classification is a central problem in High-Energy Phy...

Luca Anzalone, et al. ∙

research

∙ 05/12/2020

Prta: A System to Support the Analysis of Propaganda Techniques in the News

Recent events, such as the 2016 US Presidential Campaign, Brexit and the...

Giovanni Da San Martino, et al. ∙

research

∙ 01/21/2020

CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media

We describe the third edition of the CheckThat! Lab, which is part of th...

Alberto Barron-Cedeno, et al. ∙

research

∙ 06/25/2020

Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling

Representation learning models for graphs are a successful family of tec...

Simone Piaggesi, et al. ∙

research

∙ 10/28/2021

Neural Disparity Refinement for Arbitrary Resolution Stereo

We introduce a novel architecture for neural disparity refinement aimed ...

Filippo Aleotti, et al. ∙

research

∙ 04/28/2021

End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning

Navigating through intersections is one of the main challenging tasks fo...

Alessandro Paolo Capasso, et al. ∙

research

∙ 10/03/2018

A deep learning pipeline for product recognition on store shelves

Recognition of grocery products in store shelves poses peculiar challeng...

Alessio Tonioni, et al. ∙

research

∙ 02/02/2019

Domain invariant hierarchical embedding for grocery products recognition

Recognizing packaged grocery products based solely on appearance is stil...

Alessio Tonioni, et al. ∙

research

∙ 10/06/2019

Fine-Grained Analysis of Propaganda in News Articles

Propaganda aims at influencing people's mindset with the purpose of adva...

Giovanni Da San Martino, et al. ∙

research

∙ 01/10/2022

Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks

Keyword spotting (KWS) is a crucial function enabling the interaction wi...

Gianmarco Cerutti, et al. ∙

research

∙ 03/24/2022

Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition

Human-machine interaction is gaining traction in rehabilitation tasks, s...

Alessio Burrello, et al. ∙

research

∙ 04/07/2022

Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence

Energy-efficient machine learning models that can run directly on edge d...

Francesco Daghero, et al. ∙

research

∙ 05/24/2019

Continual Reinforcement Learning in 3D Non-stationary Environments

High-dimensional always-changing environments constitute a hard challeng...

Vincenzo Lomonaco, et al. ∙

research

∙ 01/26/2020

A Lagrangian Dual Framework for Deep Neural Networks with Constraints

A variety of computationally challenging constrained optimization proble...

Ferdinando Fioretto, et al. ∙

research

∙ 08/05/2019

Semi-Automatic Labeling for Deep Learning in Robotics

In this paper, we propose Augmented Reality Semi-automatic labeling (ARS...

Daniele De Gregorio, et al. ∙

research

∙ 04/13/2020

Technical Report: NEMO DNN Quantization for Deployment Model

This technical report aims at defining a formal framework for Deep Neura...

Francesco Conti, et al. ∙

research

∙ 01/21/2021

Content-Based Textual File Type Detection at Scale

Programming language detection is a common need in the analysis of large...

Francesca Del Bonifro, et al. ∙

research

∙ 04/12/2022

Generative Negative Replay for Continual Learning

Learning continually is a key aspect of intelligence and a necessary abi...

Gabriele Graffieti, et al. ∙

research

∙ 10/03/2018

A deep learning pipeline for product recognition in store shelves

Recognition of grocery products in store shelves poses peculiar challeng...

Alessio Tonioni, et al. ∙

research

∙ 10/15/2021

A Neural Network Ensemble Approach to System Identification

We present a new algorithm for learning unknown governing equations from...

Elisa Negrini, et al. ∙

research

∙ 02/14/2022

Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference

Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn...

Gianna Paulin, et al. ∙

research

∙ 08/19/2019

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

We propose a multi-task deep-learning approach for estimating the check-...

Slavena Vasileva, et al. ∙

research

∙ 03/23/2020

Modeling Contrary-to-Duty with CP-nets

In a ceteris-paribus semantics for deontic logic, a state of affairs whe...

Roberta Calegari, et al. ∙

research

∙ 01/24/2022

A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals

Scholarly data is growing continuously containing information about the ...

Cristian Santini, et al. ∙

research

∙ 09/29/2021

Explanation-Aware Experience Replay in Rule-Dense Environments

Human environments are often regulated by explicit and complex rulesets....

Francesco Sovrano, et al. ∙

research

∙ 03/24/2022

TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Lea...

Alessio Burrello, et al. ∙

research

∙ 08/02/2019

Space-adaptive anisotropic bivariate Laplacian regularization for image restoration

In this paper we present a new regularization term for variational image...

Luca Calatroni, et al. ∙

research

∙ 04/08/2021

Learning optical flow from still images

This paper deals with the scarcity of data for training optical flow net...

Filippo Aleotti, et al. ∙

research

∙ 04/28/2022

Morphing Attack Potential

In security systems the risk assessment in the sense of common criteria ...

Matteo Ferrara, et al. ∙

research

∙ 07/14/2022

Comparing the latent space of generative models

Different encodings of datapoints in the latent space of latent-vector g...

Andrea Asperti, et al. ∙

research

∙ 12/11/2017

The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images

The lack, due to privacy concerns, of large public databases of medical ...

Andrea Asperti, et al. ∙

research

∙ 12/04/2017

NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in ...

Paolo Meloni, et al. ∙

research

∙ 11/21/2017

Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?

Design automation in general, and in particular logic synthesis, can pla...

Manuele Rusci, et al. ∙

research

∙ 05/09/2017

CORe50: a New Dataset and Benchmark for Continuous Object Recognition

Continuous/Lifelong learning of high-dimensional data streams is a chall...

Vincenzo Lomonaco, et al. ∙

research

∙ 07/02/2014

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integra...

Giacomo Cocci, et al. ∙

research

∙ 03/14/2017

Conditional Time Series Forecasting with Convolutional Neural Networks

We present a method for conditional time series forecasting based on the...

Anastasia Borovykh, et al. ∙

research

∙ 01/31/2011

Boolean Networks Design by Genetic Algorithms

We present and discuss the results of an experimental analysis in the de...

Andrea Roli, et al. ∙

University of Bologna

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