Publications

@article{somHR2021b,

title = {Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data},

author = {Davide Bacciu and Gioele Bertoncini and Davide Morelli},

doi = {10.1007/s00521-020-05600-4},

year  = {2021},

date = {2021-01-04},

journal = {Neural Computing Applications},

publisher = {Springer-Nature},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yussupov2021_FaaSReview,

title = {FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms},

author = {Vladimir Yussupov and Jacopo Soldani and Uwe Breitenbücher and Antonio Brogi and Frank Leymann},

url = {http://www.sciencedirect.com/science/article/pii/S0164121221000030},

doi = {https://doi.org/10.1016/j.jss.2021.110906},

issn = {0164-1212},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Systems and Software},

volume = {175},

pages = {110906},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/tplp/Forti21,

title = {Probabilistic QoS-aware Placement of VNF Chains at the Edge},

author = {S Forti and F Paganelli and A Brogi},

year  = {2021},

date = {2021-01-01},

journal = {Theory Pract. Log. Program.},

note = {In Press.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{paganelli2021tenant,

title = {Tenant-defined service function chaining in a multi-site 

network slice},

author = {Federica Paganelli and Paola Cappanera and Giovanni Cuffaro},

year  = {2021},

date = {2021-01-01},

journal = {Future Generation Computer Systems},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mazzei2020blockchain,

title = {A Blockchain Tokenizer for Industrial IOT trustless applications},

author = {Daniele Mazzei and Giacomo Baldi and Gualtiero Fantoni and Gabriele Montelisciani and Antonio Pitasi and Laura Ricci and Lorenzo Rizzello},

year  = {2020},

date = {2020-01-01},

journal = {Future Generation Computer Systems},

volume = {105},

pages = {432--445},

publisher = {North-Holland},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karvelis2020portweather,

title = {PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction},

author = {Petros Karvelis and Daniele Mazzei and Matteo Biviano and Chrysostomos Stylios},

year  = {2020},

date = {2020-01-01},

journal = {Sensors},

volume = {20},

number = {11},

pages = {3181},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cerina2020echobay,

title = {EchoBay: Design and Optimization of Echo State Networks under Memory and Time Constraints},

author = {L Cerina and MD Santambrogio and G Franco and C Gallicchio and A Micheli},

year  = {2020},

date = {2020-01-01},

journal = {ACM Transactions on Architecture and Code Optimization (TACO)},

volume = {17},

number = {3},

pages = {1--24},

publisher = {ACM New York, NY, USA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BELLI2020101231,

title = {The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection},

author = {Dimitri Belli and Stefano Chessa and Luca Foschini and Michele Girolami},

url = {http://www.sciencedirect.com/science/article/pii/S1574119220300845},

doi = {https://doi.org/10.1016/j.pmcj.2020.101231},

issn = {1574-1192},

year  = {2020},

date = {2020-01-01},

journal = {Pervasive and Mobile Computing},

volume = {67},

pages = {101231},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Belli2020JIOT,

title = {A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios},

author = {D Belli and S Chessa and L Foschini and M Girolami},

doi = {10.1109/JIOT.2019.2957835},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Internet of Things Journal},

volume = {7},

number = {3},

pages = {2421-2431},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{KOCIAN2020105167,

title = {Dynamic Bayesian network for crop growth prediction in greenhouses},

url = {http://www.sciencedirect.com/science/article/pii/S0168169919321131},

doi = {https://doi.org/10.1016/j.compag.2019.105167},

issn = {0168-1699},

year  = {2020},

date = {2020-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {169},

pages = {105167},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BACCIU2020203,

title = {A gentle introduction to deep learning for graphs},

author = {Davide Bacciu and Federico Errica and Alessio Micheli and Marco Podda},

doi = {https://doi.org/10.1016/j.neunet.2020.06.006},

issn = {0893-6080},

year  = {2020},

date = {2020-01-01},

journal = {Neural Networks},

volume = {129},

pages = {203 - 221},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{toskose,

title = {Component-aware orchestration of cloud-based enterprise applications, from TOSCA to Docker and Kubernetes},

author = {Matteo Bogo and Jacopo Soldani and Davide Neri and Antonio Brogi},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.2848},

doi = {10.1002/spe.2848},

year  = {2020},

date = {2020-01-01},

journal = {Software: Practice and Experience},

volume = {50},

number = {9},

pages = {1793-1821},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wurster2020_EDMM,

title = {The essential deployment metamodel: a systematic review of deployment automation technologies},

author = {Wurster, Michael and Breitenbücher, Uwe and Falkenthal, Michael and Krieger, Christoph and Leymann, Frank and Saatkamp, Karoline and Soldani, Jacopo},

doi = {10.1007/s00450-019-00412-x},

year  = {2020},

date = {2020-01-01},

journal = {SICS Software-Intensive Cyber-Physical Systems},

volume = {35},

number = {1},

pages = {63-75},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/fgcs/FortiFB20,

title = {Secure Cloud-Edge Deployments, with Trust},

author = {Stefano Forti and Gian Luigi Ferrari and Antonio Brogi},

url = {https://doi.org/10.1016/j.future.2019.08.020},

doi = {10.1016/j.future.2019.08.020},

year  = {2020},

date = {2020-01-01},

journal = {Future Gener. Comput. Syst.},

volume = {102},

pages = {775--788},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LESORT202052,

title = {Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges},

author = {Timothée Lesort and Vincenzo Lomonaco and Andrei Stoian and Davide Maltoni and David Filliat and Natalia Díaz-Rodríguez},

url = {https://www.sciencedirect.com/science/article/pii/S1566253519307377},

doi = {https://doi.org/10.1016/j.inffus.2019.12.004},

issn = {1566-2535},

year  = {2020},

date = {2020-01-01},

journal = {Information Fusion},

volume = {58},

pages = {52-68},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/tcs/BruniMM20,

title = {Bayesian network semantics for Petri nets},

author = {Roberto Bruni and Hern á and Ugo Montanari},

year  = {2020},

date = {2020-01-01},

journal = {Theor. Comput. Sci.},

volume = {807},

pages = {95--113},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bacciu2019,

title = {Augmenting Recurrent Neural Networks Resilience by Dropout},

author = {Davide Bacciu and Francesco Crecchi},

doi = {10.1109/TNNLS.2019.2899744},

year  = {2019},

date = {2019-03-31},

journal = {IEEE Transactions on Neural Networs and Learning Systems},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8688411,

title = {GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs},

author = {T De Matteis and G Mencagli and D De Sensi and M Torquati and M Danelutto},

doi = {10.1109/ACCESS.2019.2910312},

issn = {2169-3536},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {48753-48769},

abstract = {Today's stream processing systems handle high-volume data streams in an efficient manner. To achieve this goal, they are designed to scale out on large clusters of commodity machines. However, despite the efficient use of distributed architectures, they lack support to co-processors like graphical processing units (GPUs) ready to accelerate data-parallel tasks. The main reason for this lack of integration is that GPU processing and the streaming paradigm have different processing models, with GPUs needing a bulk of data present at once while the streaming paradigm advocates a tuple-at-a-time processing model. This paper contributes to fill this gap by proposing Gasser, a system for offloading the execution of sliding-window operators on GPUs. The system focuses on completely general functions by targeting the parallel processing of non-incremental queries that are not supported by the few existing GPU-based streaming prototypes. Furthermore, Gasser provides an auto-tuning approach able to automatically find the optimal value of the configuration parameters (i.e., batch length and the degree of parallelism) needed to optimize throughput and latency with the given query and data stream. The experimental part assesses the performance efficiency of Gasser by comparing its peak throughput and latency against Apache Flink, a popular and scalable streaming system. Furthermore, we evaluate the penalty induced by supporting completely general queries against the performance achieved by the state-of-the-art solution specifically optimized for incremental queries. Finally, we show the speed and accuracy of the auto-tuning approach adopted by Gasser, which is able to self-configure the system by finding the right configuration parameters without manual tuning by the users.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8834783,

title = {Raising the Parallel Abstraction Level for Streaming Analytics Applications},

author = {G Mencagli and M Torquati and D Griebler and M Danelutto and L G L Fernandes},

doi = {10.1109/ACCESS.2019.2941183},

issn = {2169-3536},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {131944-131961},

abstract = {In the stream processing domain, applications are represented by graphs of operators arbitrarily connected and filled with their business logic code. The APIs of existing Stream Processing Systems (SPSs) ease the development of transformations that recur in the streaming practice (e.g., filtering, aggregation and joins). In contrast, their parallelism abstractions are quite limited since they provide support to stateless operators only, or when the state is organized in a set of key-value pairs. This paper presents how the parallel patterns methodology can be revisited for sliding-window streaming analytics. Our vision fosters a design process of the application as composition and nesting of ready-to-use patterns provided through a C++17 fluent interface. Our prototype implements the run-time system of the patterns in the FastFlow parallel library expressing thread-based parallelism. The experimental analysis shows interesting outcomes. First, our pattern-based approach allows easy prototyping of different versions of the application, and the programmer can leverage nesting of patterns to increase performance (up to 37% in one of the two considered test-bed cases). Second, our FastFlow implementation outperforms (three times faster) the handmade porting of our patterns in popular JVM-based SPSs. Finally, in the concluding part of this paper, we explore the use of a task-based run-time system, by deriving interesting insights into how to make our patterns library suitable for multi backends.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parvin2019,

title = {Personalized real-time anomaly detection and health feedback for older adults},

author = {Parvaneh Parvin and Stefano Chessa and Maurits Kaptein and Fabio Patern `o},

doi = {10.3233/AIS-190536},

issn = {1876-1364},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Ambient Intelligence and Smart Environments},

volume = {11},

number = {5},

pages = {453--469},

publisher = {IOS Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ci/BacciuRDGMS19,

title = {An ambient intelligence approach for learning in smart robotic environments},

author = {Davide Bacciu and Maurizio Di Rocco and Mauro Dragone and Claudio Gallicchio and Alessio Micheli and Alessandro Saffiotti},

doi = {10.1111/coin.12233},

year  = {2019},

date = {2019-01-01},

journal = {Computational Intelligence},

volume = {35},

number = {4},

pages = {1060--1087},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MALTONI201956,

title = {Continuous learning in single-incremental-task scenarios},

author = {Davide Maltoni and Vincenzo Lomonaco},

url = {https://www.sciencedirect.com/science/article/pii/S0893608019300838},

doi = {https://doi.org/10.1016/j.neunet.2019.03.010},

issn = {0893-6080},

year  = {2019},

date = {2019-01-01},

journal = {Neural Networks},

volume = {116},

pages = {56-73},

abstract = {It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single incremental task (e.g., class-incremental learning). In this paper we point out the differences between multi-task and single-incremental-task scenarios and show that well-known approaches such as LWF, EWC and SI are not ideal for incremental task scenarios. A new approach, denoted as AR1, combining architectural and regularization strategies is then specifically proposed. AR1 overhead (in terms of memory and computation) is very small thus making it suitable for online learning. When tested on CORe50 and iCIFAR-100, AR1 outperformed existing regularization strategies by a good margin.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-1912-01100,

title = {Latent Replay for Real-Time Continual Learning},

author = {Lorenzo Pellegrini and Gabriele Graffieti and Vincenzo Lomonaco and Davide Maltoni},

url = {http://arxiv.org/abs/1912.01100},

year  = {2019},

date = {2019-01-01},

journal = {CoRR},

volume = {abs/1912.01100},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chiti2019virtual,

title = {Virtual functions placement with time constraints in fog 

computing: A matching theory perspective},

author = {Francesco Chiti and Romano Fantacci and Federica Paganelli and Benedetta Picano},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Transactions on Network and Service Management},

volume = {16},

number = {3},

pages = {980--989},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CAPPANERA201924,

title = {VNF placement for service chaining in a distributed cloud 

environment with multiple stakeholders},

author = {Paola Cappanera and Federica Paganelli and Francesca Paradiso},

url = {https://www.sciencedirect.com/science/article/pii/S0140366418303104},

doi = {https://doi.org/10.1016/j.comcom.2018.10.008},

issn = {0140-3664},

year  = {2019},

date = {2019-01-01},

journal = {Computer Communications},

volume = {133},

pages = {24-40},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bacciu2018,

title = {Randomized neural networks for preference learning with physiological data},

author = {Davide Bacciu and Michele Colombo and Davide Morelli and David Plans},

doi = {10.1016/j.neucom.2017.11.070},

year  = {2018},

date = {2018-07-12},

journal = {Neurocomputing},

volume = {298},

pages = {9-20},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mazzei2018full,

title = {A full stack for quick prototyping of IoT solutions},

author = {Daniele Mazzei and Giacomo Baldi and Gabriele Montelisciani and Gualtiero Fantoni},

year  = {2018},

date = {2018-01-01},

journal = {Annals of Telecommunications},

volume = {73},

number = {7-8},

pages = {439--449},

publisher = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cominelli2018seai,

title = {SEAI: Social emotional artificial intelligence based on Damasio’s Theory of Mind},

author = {Lorenzo Cominelli and Daniele Mazzei and Danilo Emilio De Rossi},

year  = {2018},

date = {2018-01-01},

journal = {Frontiers in Robotics and AI},

volume = {5},

pages = {6},

publisher = {Frontiers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lazzeri2018designing,

title = {Designing the mind of a social robot},

author = {Nicole Lazzeri and Daniele Mazzei and Lorenzo Cominelli and Antonio Cisternino and Danilo Emilio De Rossi},

year  = {2018},

date = {2018-01-01},

journal = {Applied Sciences},

volume = {8},

number = {2},

pages = {302},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MENCAGLI2018862,

title = {Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams},

author = {Gabriele Mencagli and Massimo Torquati and Marco Danelutto},

url = {http://www.sciencedirect.com/science/article/pii/S0167739X1730938X},

doi = {https://doi.org/10.1016/j.future.2017.09.004},

issn = {0167-739X},

year  = {2018},

date = {2018-01-01},

journal = {Future Generation Computer Systems},

volume = {79},

pages = {862 - 877},

abstract = {Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MENCAGLI201874,

title = {Harnessing sliding-window execution semantics for parallel stream processing},

author = {Gabriele Mencagli and Massimo Torquati and Fabio Lucattini and Salvatore Cuomo and Marco Aldinucci},

url = {http://www.sciencedirect.com/science/article/pii/S0743731517302976},

doi = {https://doi.org/10.1016/j.jpdc.2017.10.021},

issn = {0743-7315},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Parallel and Distributed Computing},

volume = {116},

pages = {74 - 88},

abstract = {According to the recent trend in data acquisition and processing technology, big data are increasingly available in the form of unbounded streams of elementary data items to be processed in real-time. In this paper we study in detail the paradigm of sliding windows, a well-known technique for approximated queries that update their results continuously as new fresh data arrive from the stream. In this work we focus on the relationship between the various existing sliding window semantics and the way the query processing is performed from the parallelism perspective. From this study two alternative parallel models are identified, each covering semantics with very precise properties. Each model is described in terms of its pros and cons, and parallel implementations in the FastFlow framework are analyzed by discussing the layout of the concurrent data structures used for the efficient windows representation in each model.},

note = {Towards the Internet of Data: Applications, Opportunities and Future Challenges},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fault-aware-management-protocols,

title = {Fault-aware management protocols for multi-component applications},

author = {Antonio Brogi and Andrea Canciani and Jacopo Soldani},

doi = {10.1016/j.jss.2018.02.005},

issn = {0164-1212},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Systems and Software},

volume = {139},

pages = {189 - 210},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7873332,

title = {Parallel Continuous Preference Queries over Out-of-Order and Bursty Data Streams},

author = {G Mencagli and M Torquati and M Danelutto and T De Matteis},

doi = {10.1109/TPDS.2017.2679197},

issn = {1558-2183},

year  = {2017},

date = {2017-09-01},

journal = {IEEE Transactions on Parallel and Distributed Systems},

volume = {28},

number = {9},

pages = {2608-2624},

abstract = {Techniques to handle traffic bursts and out-of-order arrivals are of paramount importance to provide real-time sensor data analytics in domains like traffic surveillance, transportation management, healthcare and security applications. In these systems the amount of raw data coming from sensors must be analyzed by continuous queries that extract value-added information used to make informed decisions in real-time. To perform this task with timing constraints, parallelism must be exploited in the query execution in order to enable the real-time processing on parallel architectures. In this paper we focus on continuous preference queries, a representative class of continuous queries for decision making, and we propose a parallel query model targeting the efficient processing over out-of-order and bursty data streams. We study how to integrate punctuation mechanisms in order to enable out-of-order processing. Then, we present advanced scheduling strategies targeting scenarios with different burstiness levels, parameterized using the index of dispersion quantity. Extensive experiments have been performed using synthetic datasets and real-world data streams obtained from an existing real-time locating system. The experimental evaluation demonstrates the efficiency of our parallel solution and its effectiveness in handling the out-of-orderness degrees and burstiness levels of real-world applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bacciu2017learning,

title = {A learning system for automatic Berg Balance Scale score estimation},

author = {Davide Bacciu and Stefano Chessa and Claudio Gallicchio and Alessio Micheli and Luca Pedrelli and Erina Ferro and Luigi Fortunati and Davide La Rosa and Filippo Palumbo and Federico Vozzi and others},

year  = {2017},

date = {2017-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {66},

pages = {60--74},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CHESSA2017200,

title = {Mobile crowd sensing management with the ParticipAct living lab},

author = {Stefano Chessa and Michele Girolami and Luca Foschini and Raffaele Ianniello and Antonio Corradi and Paolo Bellavista},

url = {http://www.sciencedirect.com/science/article/pii/S1574119216302127},

doi = {https://doi.org/10.1016/j.pmcj.2016.09.005},

issn = {1574-1192},

year  = {2017},

date = {2017-01-01},

journal = {Pervasive and Mobile Computing},

volume = {38},

pages = {200 - 214},

keywords = {},

pubstate = {published},

tppubtype = {article}

}