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Book ; Online: Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks

Pasqualini, Luca / Parton, Maurizio

2020

Abstract: A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to ... ...

Abstract	A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate PRNGs quality by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state. We use a Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time steps, by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP's states. We show that modeling a PRNG with a partially observable MDP and a LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work. Comment: 14 pages, 11 figures. arXiv admin note: text overlap with arXiv:1912.11531
Keywords	Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-10-31
Publishing country	us
Document type	Book ; Online
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Book ; Online: Pseudo Random Number Generation

Pasqualini, Luca / Parton, Maurizio

a Reinforcement Learning approach

2019

Abstract: Pseudo-Random Numbers Generators (PRNGs) are algorithms produced to generate long sequences of statistically uncorrelated numbers, i.e. Pseudo-Random Numbers (PRNs). These numbers are widely employed in mid-level cryptography and in software applications. ...

Abstract	Pseudo-Random Numbers Generators (PRNGs) are algorithms produced to generate long sequences of statistically uncorrelated numbers, i.e. Pseudo-Random Numbers (PRNs). These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate PRNGs quality by checking statistical properties of the generated sequences. Machine learning techniques are often used to break these generators, for instance approximating a certain generator or a certain sequence using a neural network. But what about using machine learning to generate PRNs generators? This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve an N-dimensional navigation problem. In this context, N is the length of the period of the generated sequence, and the policy is iteratively improved using the average value of an appropriate test suite run over that period. Aim of this work is to demonstrate the feasibility of the proposed approach, to compare it with classical methods, and to lay the foundation of a research path which combines RL and PRNGs. Comment: 13 pages, 8 figures
Keywords	Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2019-12-15
Publishing country	us
Document type	Book ; Online
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Book ; Online: Generate and Revise

Zugarini, Andrea / Pasqualini, Luca / Melacci, Stefano / Maggini, Marco

Reinforcement Learning in Neural Poetry

2021

Abstract: Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. ... ...

Abstract	Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems. Comment: 12 pages, 2 figures, 5 tables
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Publishing date	2021-02-08
Publishing country	us
Document type	Book ; Online
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Book ; Online: Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

Saggese, Fabio / Pasqualini, Luca / Moretti, Marco / Abrardo, Andrea

2021

Abstract: With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper ... ...

Abstract	With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper we propose a deep reinforcement learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ proximal policy optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes. Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Keywords	Electrical Engineering and Systems Science - Signal Processing ; Computer Science - Machine Learning
Publishing date	2021-03-02
Publishing country	us
Document type	Book ; Online
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Book ; Online: Messing Up 3D Virtual Environments

Meloni, Enrico / Tiezzi, Matteo / Pasqualini, Luca / Gori, Marco / Melacci, Stefano

Transferable Adversarial 3D Objects

2021

Abstract: In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also become ...

Abstract	In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also become a mean to study the weaknesses of Machine Learning algorithms, or to simulate training settings that allow Machine Learning models to gain robustness to 3D adversarial attacks. On the other hand, their growing popularity might also attract those that aim at creating adversarial conditions to invalidate the benchmarking process, especially in the case of public environments that allow the contribution from a large community of people. Most of the existing Adversarial Machine Learning approaches are focused on static images, and little work has been done in studying how to deal with 3D environments and how a 3D object should be altered to fool a classifier that observes it. In this paper, we study how to craft adversarial 3D objects by altering their textures, using a tool chain composed of easily accessible elements. We show that it is possible, and indeed simple, to create adversarial objects using off-the-shelf limited surrogate renderers that can compute gradients with respect to the parameters of the rendering process, and, to a certain extent, to transfer the attacks to more advanced 3D engines. We propose a saliency-based attack that intersects the two classes of renderers in order to focus the alteration to those texture elements that are estimated to be effective in the target engine, evaluating its impact in popular neural classifiers. Comment: 8 pages, 7 figures, accepted for publication at the IEEE International Conference on Machine Learning and Applications (ICMLA) 2021
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	629
Publishing date	2021-09-17
Publishing country	us
Document type	Book ; Online
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Book ; Online: SAILenv

Meloni, Enrico / Pasqualini, Luca / Tiezzi, Matteo / Gori, Marco / Melacci, Stefano

Learning in Virtual Visual Environments Made Simple

2020

Abstract: Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. ... ...

Abstract	Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. However, most of the existing platforms to interface algorithms with 3D environments are often designed to setup navigation-related experiments, to study physical interactions, or to handle ad-hoc cases that are not thought to be customized, sometimes lacking a strong photorealistic appearance and an easy-to-use software interface. In this paper, we present a novel platform, SAILenv, that is specifically designed to be simple and customizable, and that allows researchers to experiment visual recognition in virtual 3D scenes. A few lines of code are needed to interface every algorithm with the virtual world, and non-3D-graphics experts can easily customize the 3D environment itself, exploiting a collection of photorealistic objects. Our framework yields pixel-level semantic and instance labeling, depth, and, to the best of our knowledge, it is the only one that provides motion-related information directly inherited from the 3D engine. The client-server communication operates at a low level, avoiding the overhead of HTTP-based data exchanges. We perform experiments using a state-of-the-art object detector trained on real-world images, showing that it is able to recognize the photorealistic 3D objects of our environment. The computational burden of the optical flow compares favourably with the estimation performed using modern GPU-based convolutional networks or more classic implementations. We believe that the scientific community will benefit from the easiness and high-quality of our framework to evaluate newly proposed algorithms in their own customized realistic conditions. Comment: 8 pages, 7 figures, submitted to ICPR 2020
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2020-07-16
Publishing country	us
Document type	Book ; Online
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Article ; Online: Monitoring of indoor bioaerosol for the detection of SARS-CoV-2 in different hospital settings.

Tedeschini, Emma / Pasqualini, Stefania / Emiliani, Carla / Marini, Ettore / Valecchi, Alessandro / Laoreti, Chiara / Ministrini, Stefano / Camilloni, Barbara / Castronari, Roberto / Patoia, Lucio / Merante, Francesco / Baglioni, Stefano / De Robertis, Edoardo / Pirro, Matteo / Mencacci, Antonella / Pasqualini, Leonella

Frontiers in public health

2023 Volume 11, Page(s) 1169073

Abstract: Background: Spore Trap is an environmental detection technology, already used in the field of allergology to monitor the presence and composition of potentially inspirable airborne micronic bioparticulate. This device is potentially suitable for ... ...

Abstract	Background: Spore Trap is an environmental detection technology, already used in the field of allergology to monitor the presence and composition of potentially inspirable airborne micronic bioparticulate. This device is potentially suitable for environmental monitoring of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in hospital, as well as in other high-risk closed environments. The aim of the present study is to investigate the accuracy of the Spore Trap system in detecting SARS-CoV-2 in indoor bioaerosol of hospital rooms. Methods: The Spore Trap was placed in hospital rooms hosting patients with documented SARS-CoV-2 infection ( Results: The estimated sensitivity of the Spore Trap device for detecting SARS-CoV-2 in an indoor environment is 69.4% (95% C.I. 54.3-84.4%), with a specificity of 100%. Conclusion: The Spore Trap technology is effective in detecting airborne SARS-CoV-2 virus with excellent specificity and high sensitivity, when compared to previous reports. The SARS-CoV-2 pandemic scenario has suggested that indoor air quality control will be a priority in future public health management and will certainly need to include an environmental bio-investigation protocol.
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19/diagnosis ; Hospitals ; Pandemics ; Hospitalization
Language	English
Publishing date	2023-04-20
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2023.1169073
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Article ; Online: Ultrasound screening for asymptomatic deep vein thrombosis in critically ill patients: a pilot trial.

Tini, Giordano / Moriconi, Amanda / Ministrini, Stefano / Zullo, Valentina / Venanzi, Elisa / Mondovecchio, Giulia / Campanella, Tommaso / Marini, Ettore / Bianchi, Maura / Carbone, Federico / Pirro, Matteo / De Robertis, Edoardo / Pasqualini, Leonella

Internal and emergency medicine

2022 Volume 17, Issue 8, Page(s) 2269–2277

Abstract: Deep vein thrombosis (DVT) in critically ill patients still represents a clinical challenge. The aim of the study was to investigate whether a systematic ultrasound (US) screening might improve the management of the antithrombotic therapy in intensive ... ...

Abstract	Deep vein thrombosis (DVT) in critically ill patients still represents a clinical challenge. The aim of the study was to investigate whether a systematic ultrasound (US) screening might improve the management of the antithrombotic therapy in intensive care unit (ICU). In this non-randomized diagnostic clinical trial, 100 patients consecutively admitted to ICU of the University Hospital of Perugia were allocated either in the screening group or in the control group. Subjects in the screening group underwent US examination of lower limbs 48 h after admission, and again after 5 days. Subjects in the control group underwent US examination according to the standard of care (SOC) of the enrolling institution. Retrospectively registered at ClinicalTrials.gov (NCT05019092) on 24.08.2021. Lower limb DVT was significantly more frequent in the screening group (p < 0.001), as well as the subsequent extension of a pre-existing DVT (p = 0.027). In the control group, DVT of large veins was more frequent (p = 0.038). Major bleedings were reported in 5 patients, 4 in the non-screening group and in 1 in the screening group. Patients in the screening group started the antithrombotic treatment later (p = 0.038), although the frequency, dose and duration of the treatment were not different between the two groups. The duration of stay in ICU was longer in the screening group (p = 0.007). Active screening for DVT is associated with an increased diagnosis of DVT. The screening could be associated with a reduced incidence of proximal DVT and a reduction in the bleeding risk.
MeSH term(s)	Humans ; Critical Illness ; Pilot Projects ; Risk Factors ; Venous Thrombosis/etiology ; Intensive Care Units
Language	English
Publishing date	2022-08-31
Publishing country	Italy
Document type	Clinical Trial ; Journal Article
ZDB-ID	2454173-4
ISSN	1970-9366 ; 1828-0447
ISSN (online)	1970-9366
ISSN	1828-0447
DOI	10.1007/s11739-022-03085-8
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Article ; Online: Effects of a 3-month weight-bearing and resistance exercise training on circulating osteogenic cells and bone formation markers in postmenopausal women with low bone mass.

Pasqualini, L / Ministrini, S / Lombardini, R / Bagaglia, F / Paltriccia, R / Pippi, R / Collebrusco, L / Reginato, E / Sbroma Tomaro, E / Marini, E / D'Abbondanza, M / Scarponi, A M / De Feo, P / Pirro, M

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA

2019 Volume 30, Issue 4, Page(s) 797–806

Abstract: Osteoporosis is a health issue in postmenopausal women. Physical activity is recommended in these subjects, since it has positive effects on bone mass. Cellular mechanisms underlying this effect are still unclear. Osteogenic cells, released after ... ...

Abstract	Osteoporosis is a health issue in postmenopausal women. Physical activity is recommended in these subjects, since it has positive effects on bone mass. Cellular mechanisms underlying this effect are still unclear. Osteogenic cells, released after physical exertion, could be a key factor in exercise-induced bone formation. Introduction: The aim of our research was to explore if a weight-bearing and resistance exercise program could positively affect circulating osteogenic cells (OCs), markers of bone formation and quality of life (QoL) in osteopenic postmenopausal women. Methods: We recruited 33 postmenopausal women with a T-score at lumbar spine or femoral neck between - 1 and - 2.5 SD. Anthropometric and fitness parameters, bone-remodeling markers, OCs, and QoL were evaluated at the time of enrolment, after 1-month run-in period, and after 3 months of weight-bearing and resistance exercise. Results: After 3 months of training, the pro-collagen type 1 N-terminal peptide (P1NP) and the number of OCs were significantly increased, with no significant increase of the type 1 collagen cross-linked C-telopeptide (sCTX). We also observed a significant increase in body height, one-repetition maximum (1RM) on the pull-down lat machine and leg press, and mean VO Conclusions: The exercise program we trialed is able to increase the markers of bone formation and the commitment of immature OCs with no significant increase in the markers of bone resorption. Our results confirm that combined weight-bearing and resistance physical activity is an effective tool to improve QoL of postmenopausal women with low bone mass. Trial registration: NCT03195517.
MeSH term(s)	Anthropometry/methods ; Biomarkers/blood ; Body Composition/physiology ; Body Height/physiology ; Bone Density/physiology ; Bone Remodeling/physiology ; Female ; Femur Neck/physiopathology ; Humans ; Lumbar Vertebrae/physiopathology ; Middle Aged ; Osteoblasts/physiology ; Osteogenesis/physiology ; Osteoporosis, Postmenopausal/pathology ; Osteoporosis, Postmenopausal/physiopathology ; Osteoporosis, Postmenopausal/rehabilitation ; Quality of Life ; Resistance Training/methods ; Weight-Bearing/physiology
Chemical Substances	Biomarkers
Language	English
Publishing date	2019-02-26
Publishing country	England
Document type	Clinical Trial ; Journal Article
ZDB-ID	1064892-6
ISSN	1433-2965 ; 0937-941X
ISSN (online)	1433-2965
ISSN	0937-941X
DOI	10.1007/s00198-019-04908-9
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Book ; Online: Score vs. Winrate in Score-Based Games

Pasqualini, Luca / Amato, Gianluca / Fantozzi, Marco / Gini, Rosa / Marchetti, Alessandro / Metta, Carlo / Morandin, Francesco / Parton, Maurizio

which Reward for Reinforcement Learning?

2022

Abstract: In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. However, when the win/lose outcome is decided by a final score difference, ... ...

Abstract	In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. However, when the win/lose outcome is decided by a final score difference, AlphaZero may play score-suboptimal moves because all winning final positions are equivalent from the win/lose outcome perspective. This can be an issue, for instance when used for teaching, or when trying to understand whether there is a better move. Moreover, there is the theoretical quest for the perfect game. A naive approach would be training an AlphaZero-like agent to predict score differences instead of win/lose outcomes. Since the game of Go is deterministic, this should as well produce an outcome-optimal play. However, it is a folklore belief that "this does not work". In this paper, we first provide empirical evidence for this belief. We then give a theoretical interpretation of this suboptimality in general perfect information two-player zero-sum game where the complexity of a game like Go is replaced by the randomness of the environment. We show that an outcome-optimal policy has a different preference for uncertainty when it is winning or losing. In particular, when in a losing state, an outcome-optimal agent chooses actions leading to a higher score variance. We then posit that when approximation is involved, a deterministic game behaves like a nondeterministic game, where the score variance is modeled by how uncertain the position is. We validate this hypothesis in AlphaZero-like software with a human expert. Comment: Published at 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). This version (v2) is a major revision and superseeds version v1
Keywords	Computer Science - Artificial Intelligence ; I.2.6
Subject code	338
Publishing date	2022-01-31
Publishing country	us
Document type	Book ; Online
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