Publications

@article{doi:10.1177/1536867X221083854,

title = {A toolbox for measuring heterogeneity and efficiency using zonotopes},

author = {Marco Cococcioni and Marco Grazzi and Le Li and Federico Ponchio},

url = {https://doi.org/10.1177/1536867X221083854},

doi = {10.1177/1536867X221083854},

year  = {2022},

date = {2022-01-01},

journal = {The Stata Journal},

volume = {22},

number = {1},

pages = {25-59},

abstract = {In this work, we describe the new command zonotope, which, by resorting to a geometry-based approach, provides a measure of productivity that fully accounts for the existing heterogeneity across firms within the same industry. The method we propose also enables assessment of the extent of multidimensional heterogeneity with applications to fields beyond that of production analysis. Finally, we detail the functioning of the software to perform the related empirical analysis, and we discuss the main computational issues encountered in its development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BenciCococcioniFiaschi+2022+65+80,

title = {Non–Standard Analysis Revisited: An Easy Axiomatic Presentation Oriented Towards Numerical Applications},

author = {Vieri Benci and Marco Cococcioni and Lorenzo Fiaschi},

url = {https://doi.org/10.34768/amcs-2022-0006},

doi = {doi:10.34768/amcs-2022-0006},

year  = {2022},

date = {2022-01-01},

journal = {International Journal of Applied Mathematics and Computer Science},

volume = {32},

number = {1},

pages = {65–80},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9583876,

title = {A Lightweight Posit Processing Unit for RISC-V Processors in Deep Neural Network Applications},

author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},

doi = {10.1109/TETC.2021.3120538},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Transactions on Emerging Topics in Computing},

volume = {10},

number = {4},

pages = {1898-1908},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9281371,

title = {TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams},

author = {Alessio Bechini and Francesco Marcelloni and Alessandro Renda},

doi = {10.1109/TFUZZ.2020.3042645},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Transactions on Fuzzy Systems},

volume = {30},

number = {3},

pages = {623-637},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jmse9111175,

title = {Game Theory for Unmanned Vehicle Path Planning in the Marine Domain: State of the Art and New Possibilities},

author = {Marco Cococcioni and Lorenzo Fiaschi and Pierre F. J. Lermusiaux},

url = {https://www.mdpi.com/2077-1312/9/11/1175},

doi = {10.3390/jmse9111175},

issn = {2077-1312},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Marine Science and Engineering},

volume = {9},

number = {11},

abstract = {Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, and civilian security are only a few of the large number of applications which currently benefit from unmanned vehicles. One of the most actively studied topic is their full autonomy; i.e., the design of marine vehicles capable of pursuing a task while reacting to the changes of the environment without the intervention of humans, not even remotely. Environmental dynamicity may consist of variations of currents, the presence of unknown obstacles, and attacks from adversaries (e.g., pirates). To achieve autonomy in such highly dynamic uncertain conditions, many types of autonomous path planning problems need to be solved. There has thus been a commensurate number of approaches and methods to optimize this kind of path planning. This work focuses on game-theoretic approaches and provides a wide overview of the current state of the art, along with future directions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BONDIELLI2021115521,

title = {On the use of summarization and transformer architectures for profiling résumés},

author = {Alessandro Bondielli and Francesco Marcelloni},

url = {https://www.sciencedirect.com/science/article/pii/S0957417421009301},

doi = {https://doi.org/10.1016/j.eswa.2021.115521},

issn = {0957-4174},

year  = {2021},

date = {2021-01-01},

journal = {Expert Systems with Applications},

volume = {184},

pages = {115521},

abstract = {Profiling professional figures is becoming more and more crucial, as companies and recruiters face the challenges of Industry 4.0. On the one hand, demand for specific knowledge in professional figures is rising. On the other hand, workers try to broaden the spectrum of their skills in order to remain appealing in the job market. Therefore, research related to these topics is receiving more and more attention. In this paper, we propose a methodology to profile résumés based on summarization and transformer architectures for generating résumé embeddings and on hierarchical clustering algorithms for grouping these embeddings. We evaluate different strategies and show that our approach achieves promising results on a public domain dataset containing 1202 résumés.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{educsci11070334,

title = {Bridges and Mediation in Higher Distance Education: HELMeTO 2020 Report},

author = {Laura Sara Agrati and Daniel Burgos and Pietro Ducange and Pierpaolo Limone and Riccardo Pecori and Loredana Perla and Pietro Picerno and Paolo Raviolo and Christian M. Stracke},

url = {https://www.mdpi.com/2227-7102/11/7/334},

doi = {10.3390/educsci11070334},

issn = {2227-7102},

year  = {2021},

date = {2021-01-01},

journal = {Education Sciences},

volume = {11},

number = {7},

abstract = {In this paper, we report the scientific experience of HELMeTO 2020, the second edition of the International Workshop on Higher Education Learning Methodologies and Technologies Online, held virtually in Bari (Italy) in September 2020 because of the COVID-19 pandemic. The call received 59 proposals from nine countries, 39 papers were accepted to the virtual workshop and 26 full papers were finally selected to be published in the proceedings. The workshop illustrated a fast-developing scenario in which the epidemic emergency accelerated the dissemination and consolidation of online learning in higher education. A specific focus of the workshop can be identified as students’ learning experience, with studies on tutoring and active learning approaches, personalized solutions supported by data analysis, virtual reality and an in-depth analysis of human–computer interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9440387,

title = {Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application},

author = {Alessio Bechini and Alessandro Bondielli and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {77758-77770},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZANON2021108023,

title = {Exploring the relations between supply chain performance and organizational culture: A fuzzy grey group decision model},

author = {Lucas Gabriel Zanon and Francesco Marcelloni and Mateus Cecílio Gerolamo and Luiz Cesar Ribeiro Carpinetti},

url = {https://www.sciencedirect.com/science/article/pii/S0925527320303728},

doi = {https://doi.org/10.1016/j.ijpe.2020.108023},

issn = {0925-5273},

year  = {2021},

date = {2021-01-01},

journal = {International Journal of Production Economics},

volume = {233},

pages = {108023},

abstract = {Assessing the relationship between supply chain performance and organizational culture can help to predict scenarios and improve decision-making. However, this relationship is rarely explored due to the complexity of quantitatively addressing its natural subjectivity. Although soft computing techniques would have the potential to overcome this limitation, they have been rarely applied to this context. This paper aims to introduce a decision model to analyze and quantify the causal relationship between organizational culture and supply chain performance based on the combination of fuzzy grey cognitive maps, grey clustering and multiple fuzzy inference systems. Such model is novel in the literature and can provide new theoretical and practical perspectives. The development of this study is based on the SCOR® (Supply Chain Operations Reference) model attributes (SCC, 2017) and Hofstede's (2001) organizational practices, following the quantitative axiomatic prescriptive model-based research. The main contribution is the introduction of a decision-making model that promotes the alignment between organizational culture and supply chain management, internalizing culture as a driver for performance improvement efforts. By conducting two real application cases in companies from different industrial sectors, results show that the model is able to identify crucial elements regarding cultural profile and performance for both organizations, aiding prioritization, anticipation and enabling the development of guidelines for action plans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ducange2021,

title = {Fuzzy Hoeffding Decision Tree for Data Stream Classification},

author = {Pietro Ducange and Francesco Marcelloni and Riccardo Pecori},

url = {https://doi.org/10.2991/ijcis.d.210212.001},

doi = {10.2991/ijcis.d.210212.001},

issn = {1875-6883},

year  = {2021},

date = {2021-01-01},

journal = {International Journal of Computational Intelligence Systems},

volume = {14},

issue = {1},

pages = {946-964},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FIASCHI2021125356,

title = {Non-Archimedean game theory: A numerical approach},

author = {Lorenzo Fiaschi and Marco Cococcioni},

url = {https://www.sciencedirect.com/science/article/pii/S0096300320303209},

doi = {https://doi.org/10.1016/j.amc.2020.125356},

issn = {0096-3003},

year  = {2021},

date = {2021-01-01},

journal = {Applied Mathematics and Computation},

volume = {409},

pages = {125356},

abstract = {In this paper we consider the Pure and Impure Prisoner’s Dilemmas. Our purpose is to theoretically extend them when using non-Archimedean quantities and to work with them numerically, potentially on a computer. The recently introduced Sergeyev’s Grossone Methodology proved to be effective in addressing our problem, because it is both a simple yet effective way to model non-Archimedean quantities and a framework which allows one to perform numerical computations between them. In addition, we could be able, in the future, to perform the same computations in hardware, resorting to the infinity computer patented by Sergeyev himself. After creating the theoretical model for Pure and Impure Prisoner’s Dilemmas using Grossone Methodology, we have numerically reproduced the diagrams associated to our two new models, using a Matlab simulator of the Infinity Computer. Finally, we have proved some theoretical properties of the simulated diagrams. Our tool is thus ready to assist the modeler in all that problems for which a non-Archimedean Pure/Impure Prisoner’s Dilemma model provides a good description of reality: energy market modeling, international trades modeling, political merging processes, etc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{COCOCCIONI2021113483,

title = {Non-Archimedean zero-sum games},

author = {Marco Cococcioni and Lorenzo Fiaschi and Luca Lambertini},

url = {https://www.sciencedirect.com/science/article/pii/S0377042721001023},

doi = {https://doi.org/10.1016/j.cam.2021.113483},

issn = {0377-0427},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Computational and Applied Mathematics},

volume = {393},

pages = {113483},

abstract = {Zero-sum games are a well known class of game theoretic models, which are widely used in several economics and engineering applications. It is known that any two-player finite zero-sum game in mixed-strategies can be solved, i.e., one of its Nash equilibria can be found solving a linear programming problem associated to it. The idea of this work is to propose and solve zero-sum games which involve infinite and infinitesimal payoffs too, that is non-Archimedean payoffs. Since to find a Nash equilibrium a non-Archimedean linear programming problem needs to be solved, we implement and extend a more powerful version of an already existing non-Archimedean Simplex algorithm, namely the Gross-Simplex one. In particular, the new algorithm, called Gross-Matrix-Simplex, is able to handle the constraint matrix A when it is made of non-Archimedean quantities. To test the correctness and the efficiency of the Gross-Matrix-Simplex algorithm, we provide four numerical experiments, which have been run on an Infinity Computer simulator. Furthermore, we stressed the difference between numerical and symbolic calculations, characterizing the solutions that an algorithm is able to output running over a finite-precision machine. In particular, we showed that the numerical solutions are particular approximations of the true Nash equilibrium which satisfy some properties which make them interestingly close to the concept of an non-Archimedean ε-Nash equilibrium. Finally, we also discuss several examples based on well known models related to economics, politics and engineering, where a non-Archimedean zero-sum model appears to be a reasonable, powerful and flexible representation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cococcioni202110575,

title = {Vectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE},

author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101901098&doi=10.1007%2fs00521-021-05814-0&partnerID=40&md5=d2e60141df6e920ad5e0683713bfc7ea},

doi = {10.1007/s00521-021-05814-0},

year  = {2021},

date = {2021-01-01},

journal = {Neural Computing and Applications},

volume = {33},

number = {16},

pages = {10575 – 10585},

abstract = {With the arrival of the open-source RISC-V processor architecture, there is the chance to rethink Deep Neural Networks (DNNs) and information representation and processing. In this work, we will exploit the following ideas: i) reduce the number of bits needed to represent the weights of the DNNs using our recent findings and implementation of the posit number system, ii) exploit RISC-V vectorization as much as possible to speed up the format encoding/decoding, the evaluation of activations functions (using only arithmetic and logic operations, exploiting approximated formulas) and the computation of core DNNs matrix-vector operations. The comparison with the well-established architecture ARM Scalable Vector Extension is natural and challenging due to its closedness and mature nature. The results show how it is possible to vectorize posit operations on RISC-V, gaining a substantial speed-up on all the operations involved. Furthermore, the experimental outcomes highlight how the new architecture can catch up, in terms of performance, with the more mature ARM architecture. Towards this end, the present study is important because it anticipates the results that we expect to achieve when we will have an open RISC-V hardware co-processor capable to operate natively with posits. © 2021, The Author(s).},

note = {Cited by: 6; All Open Access, Green Open Access, Hybrid Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9388902,

title = {Solving Mixed Pareto-Lexicographic Multiobjective Optimization Problems: The Case of Priority Levels},

author = {Leonardo Lai and Lorenzo Fiaschi and Marco Cococcioni and Kalyanmoy Deb},

doi = {10.1109/TEVC.2021.3068816},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Evolutionary Computation},

volume = {25},

number = {5},

pages = {971-985},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{app11104697,

title = {Explainable Internet Traffic Classification},

author = {Christian Callegari and Pietro Ducange and Michela Fazzolari and Massimo Vecchio},

url = {https://www.mdpi.com/2076-3417/11/10/4697},

doi = {10.3390/app11104697},

issn = {2076-3417},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {10},

abstract = {The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{app11136232,

title = {Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators},

author = {Lorenzo De Marinis and Marco Cococcioni and Odile Liboiron-Ladouceur and Giampiero Contestabile and Piero Castoldi and Nicola Andriolli},

url = {https://www.mdpi.com/2076-3417/11/13/6232},

doi = {10.3390/app11136232},

issn = {2076-3417},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {13},

abstract = {Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency.},

keywords = {},

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tppubtype = {article}

}

@article{9307291,

title = {Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities},

author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara and Benoit Dupont de Dinechin},

doi = {10.1109/MSP.2020.2988436},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Signal Processing Magazine},

volume = {38},

number = {1},

pages = {97-110},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9294076,

title = {Stance Analysis of Twitter Users: The Case of the Vaccination Topic in Italy},

author = {Alessio Bechini and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},

doi = {10.1109/MIS.2020.3044968},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Intelligent Systems},

volume = {36},

number = {5},

pages = {131-139},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cococcioni20212455,

title = {The Big-M method with the numerical infinite M},

author = {Marco Cococcioni and Lorenzo Fiaschi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091160412&doi=10.1007%2fs11590-020-01644-6&partnerID=40&md5=77cf39493c13a21f98f05f48aa45c4c1},

doi = {10.1007/s11590-020-01644-6},

year  = {2021},

date = {2021-01-01},

journal = {Optimization Letters},

volume = {15},

number = {7},

pages = {2455 – 2468},

abstract = {Linear programming is a very well known and deeply applied field of optimization theory. One of its most famous and used algorithms is the so called Simplex algorithm, independently proposed by Kantorovič and Dantzig, between the end of the 30s and the end of the 40s. Even if extremely powerful, the Simplex algorithm suffers of one initialization issue: its starting point must be a feasible basic solution of the problem to solve. To overcome it, two approaches may be used: the two-phases method and the Big-M method, both presenting positive and negative aspects. In this work we aim to propose a non-Archimedean and non-parametric variant of the Big-M method, able to overcome the drawbacks of its classical counterpart (mainly, the difficulty in setting the right value for the constant M). We realized such extension by means of the novel computational methodology proposed by Sergeyev, known as Grossone Methodology. We have validated the new algorithm by testing it on three linear programming problems. © 2020, The Author(s).},

note = {Cited by: 22; All Open Access, Hybrid Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ducange2020,

title = {An overview of recent distributed algorithms for learning fuzzy models in Big Data classification},

author = {Pietro Ducange and Michela Fazzolari and Francesco Marcelloni},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081548103&doi=10.1186%2fs40537-020-00298-6&partnerID=40&md5=3bb17ce25b272147a61a4c6def21b5c5},

doi = {10.1186/s40537-020-00298-6},

year  = {2020},

date = {2020-01-01},

journal = {Journal of Big Data},

volume = {7},

number = {1},

abstract = {Nowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability. © 2020, The Author(s).},

note = {Cited by: 16; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BARSACCHI2020113436,

title = {An analysis of boosted ensembles of binary fuzzy decision trees},

author = {Marco Barsacchi and Alessio Bechini and Francesco Marcelloni},

url = {https://www.sciencedirect.com/science/article/pii/S0957417420302608},

doi = {https://doi.org/10.1016/j.eswa.2020.113436},

issn = {0957-4174},

year  = {2020},

date = {2020-01-01},

journal = {Expert Systems with Applications},

volume = {154},

pages = {113436},

abstract = {Classification is a functionality that plays a central role in the development of modern expert systems, across a wide variety of application fields: using accurate, efficient, and compact classification models is often a prime requirement. Boosting (and AdaBoost in particular) is a well-known technique to obtain robust classifiers from properly-learned weak classifiers, thus it is particularly attracting in many practical settings. Although the use of traditional classifiers as base learners in AdaBoost has already been widely studied, the adoption of fuzzy weak learners still requires further investigations. In this paper we describe FDT-Boost, a boosting approach shaped according to the SAMME-AdaBoost scheme, which leverages fuzzy binary decision trees as multi-class base classifiers. Such trees are kept compact by constraining their depth, without lowering the classification accuracy. The experimental evaluation of FDT-Boost has been carried out using a benchmark containing eighteen classification datasets. Comparing our approach with FURIA, one of the most popular fuzzy classifiers, with a fuzzy binary decision tree, and with a fuzzy multi-way decision tree, we show that FDT-Boost is accurate, getting to results that are statistically better than those achieved by the other approaches. Moreover, compared to a crisp SAMME-AdaBoost implementation, FDT-Boost shows similar performances, but the relative produced models are significantly less complex, thus opening up further exploitation chances also in memory-constrained systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LAI2020100687,

title = {Solving mixed Pareto-Lexicographic multi-objective optimization problems: The case of priority chains},

author = {Leonardo Lai and Lorenzo Fiaschi and Marco Cococcioni},

url = {https://www.sciencedirect.com/science/article/pii/S2210650219303086},

doi = {https://doi.org/10.1016/j.swevo.2020.100687},

issn = {2210-6502},

year  = {2020},

date = {2020-01-01},

journal = {Swarm and Evolutionary Computation},

volume = {55},

pages = {100687},

abstract = {This paper introduces a new class of optimization problems, called Mixed Pareto-Lexicographic Multi-objective Optimization Problems (MPL-MOPs), to provide a suitable model for scenarios where some objectives have priority over some others. Specifically, this work focuses on a relevant subclass of MPL-MOPs, namely problems involving Pareto optimization of two or more priority chains. A priority chain (PC) is a sequence of objectives lexicographically ordered by importance. After examining the main features of those problems, named PC-MPL-MOPs, we propose an innovative approach to deal with them, built upon the Grossone Methodology, a recent theory which enables handling the priority in an elegant and powerful way. The most interesting aspect of this technique is the possibility to seamlessly embed it in any existing evolutionary algorithm, without altering its logical structure. In order to provide concrete examples, we implemented it on top of the well-known NSGA-II and MOEA/D algorithms, calling these new generalized versions PC-NSGA-II and PC-MOEA/D, respectively. In the second part of this article, we test the strength of our strategy in solving multi- and even many-objective problems with priority chains, comparing it against the results achieved by standard priority-based and non-priority-based approaches. Experiments show that our algorithms are generally able to produce more solutions and of higher quality.},

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tppubtype = {article}

}

@article{COCOCCIONI2020105177,

title = {Solving the Lexicographic Multi-Objective Mixed-Integer Linear Programming Problem using branch-and-bound and grossone methodology},

author = {Marco Cococcioni and Alessandro Cudazzo and Massimo Pappalardo and Yaroslav D. Sergeyev},

url = {https://www.sciencedirect.com/science/article/pii/S1007570420300125},

doi = {https://doi.org/10.1016/j.cnsns.2020.105177},

issn = {1007-5704},

year  = {2020},

date = {2020-01-01},

journal = {Communications in Nonlinear Science and Numerical Simulation},

volume = {84},

pages = {105177},

abstract = {In the previous work (see [1]) the authors have shown how to solve a Lexicographic Multi-Objective Linear Programming (LMOLP) problem using the Grossone methodology described in [2]. That algorithm, called GrossSimplex, was a generalization of the well-known simplex algorithm, able to deal numerically with infinitesimal/infinite quantities. The aim of this work is to provide an algorithm able to solve a similar problem, with the addition of the constraint that some of the decision variables have to be integer. We have called this problem LMOMILP (Lexicographic Multi-Objective Mixed-Integer Linear Programming). This new problem is solved by introducing the GrossBB algorithm, which is a generalization of the Branch-and-Bound (BB) algorithm. The new method is able to deal with lower-bound and upper-bound estimates which involve infinite and infinitesimal numbers (namely, Grossone-based numbers). After providing theoretical conditions for its correctness, it is shown how the new method can be coupled with the GrossSimplex algorithm described in [1], to solve the original LMOMILP problem. To illustrate how the proposed algorithm finds the optimal solution, a series of LMOMILP benchmarks having a known solution is introduced. In particular, it is shown that the GrossBB combined with the GrossSimplex is able solve the proposed LMOMILP test problems with up to 200 objectives.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cococcioni2020759,

title = {Fast deep neural networks for image processing using posits and ARM scalable vector extension},

author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085561915&doi=10.1007%2fs11554-020-00984-x&partnerID=40&md5=5464de0bcfe22d628d103beb97124fbf},

doi = {10.1007/s11554-020-00984-x},

year  = {2020},

date = {2020-01-01},

journal = {Journal of Real-Time Image Processing},

volume = {17},

number = {3},

pages = {759 – 771},

abstract = {With the advent of image processing and computer vision for automotive under real-time constraints, the need for fast and architecture-optimized arithmetic operations is crucial. Alternative and efficient representations for real numbers are starting to be explored, and among them, the recently introduced positTM number system is highly promising. Furthermore, with the implementation of the architecture-specific mathematical library thoroughly targeting single-instruction multiple-data (SIMD) engines, the acceleration provided to deep neural networks framework is increasing. In this paper, we present the implementation of some core image processing operations exploiting the posit arithmetic and the ARM scalable vector extension SIMD engine. Moreover, we present applications of real-time image processing to the autonomous driving scenario, presenting benchmarks on the tinyDNN deep neural network (DNN) framework. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {Cited by: 10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}