Publications
Here's a list of my publications, ordered by year.
A useful link is my profile on Google Scholar:
Icons description: J="Journal article", C="Conference paper", B="Book chapter", X="Pre-print".
Last update: 2024-12-23.
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J
2024
Naturalness indicators of forests in Southern Sweden derived from the canopy height model
M. L. Della Vedova, M. Wahde
European Journal of Remote Sensing, 58(1):2441834, December 2024.
doi:10.1080/22797254.2024.2441834
Forest canopies embody a dynamic set of ecological factors, acting as a pivotal interface between the Earth and its atmosphere. They are not only the result of an ecosystem’s ability to maintain its inherent ecological processes, structures, and functions but also a reflection of human disturbance. This study introduces a methodology for extracting a comprehensive and human-interpretable set of features from the Canopy Height Model (CHM) with a resolution of 1 meter. These features are then analyzed to identify reliable indicators of the degree of naturalness of forests in Southern Sweden. Using these features, machine learning models – specifically, the perceptron, logistic regression, and decision trees – are trained with examples of forests exhibiting known high and low degrees of naturalness. These models achieve prediction accuracies ranging from 89\% to 95\% on unseen data, depending on the area of the region of interest. The predictions of the proposed method are easy to interpret, making them particularly valuable to various stakeholders involved in forest management and conservation.@article{10.1080/22797254.2024.2441834, author = "Della Vedova, Marco L. and Wahde, Mattias", title = "Naturalness indicators of forests in {Southern} {Sweden} derived from the canopy height model", volume = "58", issn = "null", url = "https://doi.org/10.1080/22797254.2024.2441834", doi = "10.1080/22797254.2024.2441834", number = "1", urldate = "2024-12-23", journal = "European Journal of Remote Sensing", month = "December", year = "2024", keywords = "canopy height model, forests, interpretability, Machine learning, remote sensing", pages = "2441834" } -
x
2024
Naturalness Indicators of Forests in Southern Sweden derived from the Canopy Height Model
M. L. Della Vedova, M. Wahde
arXiv, October 2024.arXiv:2410.10465 [cs].
Forest canopies embody a dynamic set of ecological factors, acting as a pivotal interface between the Earth and its atmosphere. They are not only the result of an ecosystem's ability to maintain its inherent ecological processes, structures, and functions but also a reflection of human disturbance. This study introduces a methodology for extracting a comprehensive and human-interpretable set of features from the Canopy Height Model (CHM), which are then analyzed to identify reliable indicators for the degree of naturalness of forests in Southern Sweden. Utilizing these features, machine learning models - specifically, the perceptron, logistic regression, and decision trees - are applied to predict forest naturalness with an accuracy spanning from 89\% to 95\%, depending on the area of the region of interest. The predictions of the proposed method are easy to interpret, something that various stakeholders may find valuable.@misc{10.48550/arXiv.2410.10465, author = "Della Vedova, Marco L. and Wahde, Mattias", title = "Naturalness {Indicators} of {Forests} in {Southern} {Sweden} derived from the {Canopy} {Height} {Model}", url = "http://arxiv.org/abs/2410.10465", doi = "10.48550/arXiv.2410.10465", urldate = "2024-12-23", publisher = "arXiv", month = "October", year = "2024", note = "arXiv:2410.10465 [cs]", keywords = "Computer Science - Computational Engineering, Finance, and Science" } -
J
2024
Joint structure learning and causal effect estimation for categorical graphical models
F. Castelletti, G. Consonni, M. L. Della Vedova
Biometrics, 80(3):ujae067, September 2024.
The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.@article{10.1093/biomtc/ujae067, author = "Castelletti, Federico and Consonni, Guido and Della Vedova, Marco L.", title = "Joint structure learning and causal effect estimation for categorical graphical models", volume = "80", issn = "0006-341X", url = "https://doi.org/10.1093/biomtc/ujae067", doi = "10.1093/biomtc/ujae067", number = "3", urldate = "2024-07-29", journal = "Biometrics", month = "September", year = "2024", pages = "ujae067" } -
C
2024
A Challenging Data Set for Evaluating Part-of-speech Taggers
M. Wahde, M. Suvanto, M. L. Della Vedova
In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 79–86. Roma, Italy, February 2024. SciTePress.
We introduce a novel, challenging test set for part-of-speech (POS) tagging, consisting of sentences in which only one word is POS-tagged. First derived from Wiktionary, and then manually curated, it is intended as an out-of-sample test set for POS taggers trained over larger data sets. Sentences were selected such that at least one of four standard benchmark taggers would incorrectly tag the word under consideration for a given sentence, thus identifying challenging instances of POS tagging. Somewhat surprisingly, we find that the benchmark taggers often fail on rather straightforward instances of POS tagging, and we analyze these failures in some detail. We also compute the performance of a state-of-the-art DNN-based POS tagger over our set, obtaining an accuracy of around 0.87 for this out-of-sample test, far below its reported performance in the literature. Also for this tagger, we find instances of failure even in rather simple cases.@inproceedings{10.5220/0012307200003636, author = "Wahde, Mattias and Suvanto, Minerva and Della Vedova, Marco L.", address = "Roma, Italy", title = "A {Challenging} {Data} {Set} for {Evaluating} {Part}-of-speech {Taggers}", copyright = "CC BY-NC-ND 4.0", isbn = "978-989-758-680-4", url = "https://www.scitepress.org/Link.aspx?doi=10.5220/0012307200003636", doi = "10.5220/0012307200003636", booktitle = "Proceedings of the 16th {International} {Conference} on {Agents} and {Artificial} {Intelligence} - {Volume} 2: {ICAART}", publisher = "SciTePress", month = "February", year = "2024", pages = "79--86" } -
x
2024
Indicators for characterising online hate speech and its automatic detection
E. Forzinetti, M. L. Della Vedova, S. Pasta, M. Santerini
arXiv, February 2024.arXiv:2402.08462 [cs].
We examined four case studies in the context of hate speech on Twitter in Italian from 2019 to 2020, aiming at comparing the classification of the 3,600 tweets made by expert pedagogists with the automatic classification made by machine learning algorithms. Pedagogists used a novel classification scheme based on seven indicators that characterize hate. These indicators are: the content is public, it affects a target group, it contains hate speech in explicit verbal form, it will not redeem, it has intention to harm, it can have a possible violent response, it incites hatred and violence. The case studies refer to Jews, Muslims, Roma, and immigrants target groups. We find that not all the types of hateful content are equally detectable by the machine learning algorithms that we considered. In particular, algorithms perform better in identifying tweets that incite hatred and violence, and those that can have possible violent response.@misc{10.48550/arXiv.2402.08462, author = "Forzinetti, Erica and Della Vedova, Marco L. and Pasta, Stefano and Santerini, Milena", title = "Indicators for characterising online hate speech and its automatic detection", doi = "10.48550/arXiv.2402.08462", publisher = "arXiv", month = "February", year = "2024", note = "arXiv:2402.08462 [cs]", keywords = "Computer Science - Social and Information Networks" } -
x
2023
Joint structure learning and causal effect estimation for categorical graphical models
F. Castelletti, G. Consonni, M. L. Della Vedova
arXiv, June 2023.
We consider a a collection of categorical random variables. Of special interest is the causal effect on an outcome variable following an intervention on another variable. Conditionally on a Directed Acyclic Graph (DAG), we assume that the joint law of the random variables can be factorized according to the DAG, where each term is a categorical distribution for the node-variable given a configuration of its parents. The graph is equipped with a causal interpretation through the notion of interventional distribution and the allied "do-calculus". From a modeling perspective, the likelihood is decomposed into a product over nodes and parents of DAG-parameters, on which a suitably specified collection of Dirichlet priors is assigned. The overall joint distribution on the ensemble of DAG-parameters is then constructed using global and local independence. We account for DAG-model uncertainty and propose a reversible jump Markov Chain Monte Carlo (MCMC) algorithm which targets the joint posterior over DAGs and DAG-parameters; from the output we are able to recover a full posterior distribution of any causal effect coefficient of interest, possibly summarized by a Bayesian Model Averaging (BMA) point estimate. We validate our method through extensive simulation studies, wherein comparisons with alternative state-of-the-art procedures reveal an outperformance in terms of estimation accuracy. Finally, we analyze a dataset relative to a study on depression and anxiety in undergraduate students.@misc{10.48550/arXiv.2306.16068, author = "Castelletti, Federico and Consonni, Guido and Della Vedova, Marco L.", title = "Joint structure learning and causal effect estimation for categorical graphical models", url = "http://arxiv.org/abs/2306.16068", doi = "10.48550/arXiv.2306.16068", urldate = "2024-01-17", publisher = "arXiv", month = "June", year = "2023", keywords = "Statistics - Computation, Statistics - Methodology" } -
J
2023
An interpretable method for automated classification of spoken transcripts and written text
M. Wahde, M. L. Della Vedova, M. Virgolin, M. Suvanto
Evolutionary Intelligence, May 2023.
doi:10.1007/s12065-023-00851-1
We investigate the differences between spoken language (in the form of radio show transcripts) and written language (Wikipedia articles) in the context of text classification. We present a novel, interpretable method for text classification, involving a linear classifier using a large set of n-gram features, and apply it to a newly generated data set with sentences originating either from spoken transcripts or written text. Our classifier reaches an accuracy less than 0.02 below that of a commonly used classifier (DistilBERT) based on deep neural networks (DNNs). Moreover, our classifier has an integrated measure of confidence, for assessing the reliability of a given classification. An online tool is provided for demonstrating our classifier, particularly its interpretable nature, which is a crucial feature in classification tasks involving high-stakes decision-making. We also study the capability of DistilBERT to carry out fill-in-the-blank tasks in either spoken or written text, and find it to perform similarly in both cases. Our main conclusion is that, with careful improvements, the performance gap between classical methods and DNN-based methods may be reduced significantly, such that the choice of classification method comes down to the need (if any) for interpretability.@article{10.1007/s12065-023-00851-1, author = "Wahde, Mattias and Della Vedova, Marco L. and Virgolin, Marco and Suvanto, Minerva", title = "An interpretable method for automated classification of spoken transcripts and written text", issn = "1864-5909, 1864-5917", url = "https://link.springer.com/10.1007/s12065-023-00851-1", doi = "10.1007/s12065-023-00851-1", language = "en", journal = "Evolutionary Intelligence", month = "May", year = "2023" } -
J
2022
Power sum polynomials and the ghosts behind them
M. L. Della Vedova, S. M. C. Pagani, S. Pianta
Journal of Geometry, 114(1):1, November 2022.
doi:10.1007/s00022-022-00662-2
The power sum polynomial associated to a multi-subset of the projective plane \$\$\textbackslashtext \PG\(2,q)\$\$is the sum of the \$\$(q-1)\$\$-th powers of the Rédei factors of the points in the multi-subset. The classification of multi-subsets having the same power sum polynomial passes through the determination of those multi-subsets associated to the zero polynomial, called ghosts. In this paper we provide new classes of ghosts and compute the dimension of the ghost subspace by exploiting the linear code generated by the lines of \$\$\textbackslashtext \PG\(2,q)\$\$and its dual. Moreover, we explicitly enumerate and classify ghosts for planes of order 2, 3, 4.@article{10.1007/s00022-022-00662-2, author = "Della Vedova, Marco L. and Pagani, Silvia M. C. and Pianta, Silvia", title = "Power sum polynomials and the ghosts behind them", volume = "114", issn = "1420-8997", url = "https://doi.org/10.1007/s00022-022-00662-2", doi = "10.1007/s00022-022-00662-2", language = "en", number = "1", urldate = "2023-01-01", journal = "Journal of Geometry", month = "November", year = "2022", keywords = "05B25, 11T06, 51E30, Ghost, multiset sum, power sum polynomial, Primary 51E15, projective plane, Secondary 51E22", pages = "1" } -
J
2022
A Methodology for Controlling Bias and Fairness in Synthetic Data Generation
E. Barbierato, M. L. Della Vedova, D. Tessera, D. Toti, N. Vanoli
Applied Sciences, 12(9):4619, January 2022.Number: 9 Publisher: Multidisciplinary Digital Publishing Institute.
The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and evaluate these problems, less attention has been dedicated to methods generating intentionally biased datasets, which could be used by data scientists to develop and validate unbiased and fair decision-making algorithms. To this end, this paper presents a novel method to generate a synthetic dataset, where bias can be modeled by using a probabilistic network exploiting structural equation modeling. The proposed methodology has been validated on a simple dataset to highlight the impact of tuning parameters on bias and fairness, as well as on a more realistic example based on a loan approval status dataset. In particular, this methodology requires a limited number of parameters compared to other techniques for generating datasets with a controlled amount of bias and fairness.@article{10.3390/app12094619, author = "Barbierato, Enrico and Della Vedova, Marco L. and Tessera, Daniele and Toti, Daniele and Vanoli, Nicola", title = "A {Methodology} for {Controlling} {Bias} and {Fairness} in {Synthetic} {Data} {Generation}", volume = "12", copyright = "http://creativecommons.org/licenses/by/3.0/", issn = "2076-3417", url = "https://www.mdpi.com/2076-3417/12/9/4619", doi = "10.3390/app12094619", language = "en", number = "9", urldate = "2022-07-21", journal = "Applied Sciences", month = "January", year = "2022", note = "Number: 9 Publisher: Multidisciplinary Digital Publishing Institute", keywords = "machine learning, bias, data generation, fairness, structural equation modeling", pages = "4619" } -
J
2021
S. Pasta, M. Santerini, E. Forzinetti, M. L. Della Vedova
Form@re - Open Journal per la formazione in rete, 21(3):288–304, December 2021.
The article presents a case study on Antisemitic hate speech in Twitter in the period September 2019 - May 2020, with a particular focus on the months of the Covid-19 emergency. The corpus, consisting of 160.646 tweets selected by keywords, was investigated in terms of the amount of hate for each month, rhetoric and forms of Antisemitism. The analysis is carried out through social network analysis (SNA) techniques, with the goal of understanding whether it is possible to automate the process of identifying Antisemitic hatred. 26.11\% of tweets contain hatred, that prejudice is the most common rhetoric (44\%) and association with financial power the prevailing form (74\%). The sample was also compared with another research methodology that only detects the presence of hate words. It emerges that, in addition to an in-depth knowledge of the phenomenon, it is necessary to integrate the automatic classification phase with the manual contribution. Antisemitismo e Covid-19 in Twitter. La ricerca dell’odio online tra automatismi e valutazione qualitativa. L’articolo presenta un caso studio sul discorso d’odio antisemita in Twitter nel periodo settembre 2019 - maggio 2020, con un particolare affondo sui mesi dell’emergenza Covid-19. Il corpus, composto da 160.646 tweet selezionati per parole chiave, è stato indagato in termini di quantità di odio per mese, retoriche utilizzate e forme di antisemitismo. L’analisi è svolta attraverso le tecniche di social network analysis (SNA), con l’obiettivo di capire se sia possibile automatizzare il processo di individuazione dell’odio antisemita. Il 26.11\% dei tweet contiene odio, che il pregiudizio è la retorica più presente (44\%) e l’associazione al potere finanziario la forma prevalente (74\%). Il campione è stato altresì confrontato con un’altra metodologia di ricerca che rileva la sola presenza di hate words. Emerge che, oltre una conoscenza approfondita del fenomeno, occorre integrare la fase di classificazione automatica con l’apporto manuale.@article{10.36253/form-9990, author = "Pasta, Stefano and Santerini, Milena and Forzinetti, Erica and Della Vedova, Marco L.", title = "Antisemitism and {Covid}-19 on {Twitter}. {The} search for hatred online between automatisms and qualitative evaluation", volume = "21", issn = "1825-7321", url = "https://oaj.fupress.net/index.php/formare/article/view/9990", doi = "10.36253/form-9990", number = "3", urldate = "2022-04-20", journal = "Form@re - Open Journal per la formazione in rete", month = "December", year = "2021", pages = "288--304" } -
J
2021
Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds
M. C. Calzarossa, M. L. Della Vedova, L. Massari, G. Nebbione, D. Tessera
IEEE Access, 9:89891–89905, June 2021.
doi:10.1109/ACCESS.2021.3091310
Cloud technologies are being used nowadays to cope with the increased computing and storage requirements of services and applications. Nevertheless, decisions about resources to be provisioned and the corresponding scheduling plans are far from being easily made especially because of the variability and uncertainty affecting workload demands as well as technological infrastructure performance. In this paper we address these issues by formulating a multi-objective constrained optimization problem aimed at identifying the optimal scheduling plans for scientific workflows to be deployed in uncertain cloud environments. In particular, we focus on minimizing the expected workflow execution time and monetary cost under probabilistic constraints on deadline and budget. According to the proposed approach, this problem is solved offline, that is, prior to workflow execution, with the intention of allowing cloud users to choose the plan of the Pareto optimal set satisfying their requirements and preferences. The analysis of the combined effects of cloud uncertainty and probabilistic constraints has shown that the solutions of the optimization problem are strongly affected by uncertainty. Hence, to properly provision cloud resources, it is compelling to precisely quantify uncertainty and take explicitly into account its effects in the decision process.@article{10.1109/ACCESS.2021.3091310, author = "Calzarossa, Maria Carla and Della Vedova, Marco L. and Massari, Luisa and Nebbione, Giuseppe and Tessera, Daniele", title = "Multi-{Objective} {Optimization} of {Deadline} and {Budget}-{Aware} {Workflow} {Scheduling} in {Uncertain} {Clouds}", volume = "9", issn = "2169-3536", doi = "10.1109/ACCESS.2021.3091310", journal = "IEEE Access", month = "June", year = "2021", keywords = "Cloud computing, Probabilistic logic, Processor scheduling, Uncertainty, Optimal scheduling, Scheduling, Task analysis, scientific workflows, Genetic Algorithm, Monte Carlo method, multi-objective constrained optimization, uncertainty", pages = "89891--89905" } -
J
2021
M. L. Della Vedova
Dizionario di dottrina sociale della Chiesa, pages 170–175, March 2021.
Un algoritmo è la descrizione di una procedura per risolvere un problema attraverso una sequenza finita di passi elementari. Di particolare interesse sono gli algoritmi “computazionali” perché possono essere eseguiti da un computer. Si può affermare che tutto (e solo) quello che un computer fa è eseguire algoritmi.@article{10.26350/dizdott_000020, author = "Della Vedova, Marco L.", title = "Algoritmo", url = "https://www.dizionariodottrinasociale.it/Voci/Algoritmo.html", doi = "10.26350/dizdott\_000020", language = "ita", number = "1", journal = "Dizionario di dottrina sociale della Chiesa", month = "March", year = "2021", pages = "170--175" } -
C
2019
Modeling and predicting dynamics of heterogeneous workloads for cloud environments
M. C. Calzarossa, M. L. Della Vedova, L. Massari, G. Nebbione, D. Tessera
In 2019 IEEE Symposium on Computers and Communications (ISCC), 1–7. Barcelona, Spain, June 2019. IEEE.
doi:10.1109/ISCC47284.2019.8969761
The services and applications deployed nowadays in cloud environments are characterized by variable intensity and resource requirements. The variability of these workloads coupled with their heterogeneity affects the cost associated with the cloud infrastructure and the performance levels that can be satisfied. In these complex scenarios, resource provisioning policies have to take into account the actual workloads being processed and pro-actively anticipate in a timely manner the changes in workload intensity and characteristics. To support this decision process, we propose an integrated approach - that combines various workload characterization techniques - for modeling and predicting workload access patterns. The application of this approach has shown the importance of identifying models that specifically capture and reproduce the dynamics of these patterns and consider at the same time their peculiarities.@inproceedings{10.1109/ISCC47284.2019.8969761, author = "Calzarossa, Maria Carla and Della Vedova, Marco L. and Massari, Luisa and Nebbione, Giuseppe and Tessera, Daniele", address = "Barcelona, Spain", title = "Modeling and predicting dynamics of heterogeneous workloads for cloud environments", isbn = "978-1-7281-2999-0", doi = "10.1109/ISCC47284.2019.8969761", booktitle = "2019 {IEEE} {Symposium} on {Computers} and {Communications} ({ISCC})", publisher = "IEEE", month = "June", year = "2019", pages = "1--7" } -
J
2019
M. C. Calzarossa, M. L. Della Vedova, D. Tessera
Future Generation Computer Systems, 93:212–223, April 2019.
doi:10.1016/j.future.2018.10.037
Data parallel applications are being extensively deployed in cloud environments because of the possibility of dynamically provisioning storage and computation resources. To identify cost-effective solutions that satisfy the desired service levels, resource provisioning and scheduling play a critical role. Nevertheless, the unpredictable behavior of cloud performance makes the estimation of the resources actually needed quite complex. In this paper we propose a provisioning and scheduling framework that explicitly tackles uncertainties and performance variability of the cloud infrastructure and of the workload. This framework allows cloud users to estimate in advance, i.e., prior to the actual execution of the applications, the resource settings that cope with uncertainty. We formulate an optimization problem where the characteristics not perfectly known or affected by uncertain phenomena are represented as random variables modeled by the corresponding probability distributions. Provisioning and scheduling decisions – while optimizing various metrics, such as monetary leasing costs of cloud resources and application execution time – take fully account of uncertainties encountered in cloud environments. To test our framework, we consider data parallel applications characterized by a deadline constraint and we investigate the impact of their characteristics and of the variability of the cloud infrastructure. The experiments show that the resource provisioning and scheduling plans identified by our approach nicely cope with uncertainties and ensure that the application deadline is satisfied.@article{10.1016/j.future.2018.10.037, author = "Calzarossa, Maria Carla and Della Vedova, Marco L. and Tessera, Daniele", title = "A methodological framework for cloud resource provisioning and scheduling of data parallel applications under uncertainty", volume = "93", issn = "0167739X", doi = "10.1016/j.future.2018.10.037", language = "en", journal = "Future Generation Computer Systems", month = "April", year = "2019", pages = "212--223" } -
C
2019
M. C. Calzarossa, L. Massari, G. Nebbione, M. L. Della Vedova, D. Tessera
In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 174–180. Pavia, Italy, February 2019. IEEE.
doi:10.1109/EMPDP.2019.8671564
Cloud computing allows users to devise cost-effective solutions for deploying their applications. Nevertheless, the decisions about resource provisioning are very challenging because workloads are seriously affected by the uncertainty of cloud performance and their characteristics vary. In this paper we address these issues by explicitly modeling workload and cloud uncertainty in the decision process. For this purpose, we adopt a probabilistic formulation of the optimization problem aimed at minimizing the expected cost for deploying a parallel application under a deadline constraint. To find a sub-optimal solution of the problem we apply a Genetic Algorithm. By tuning its parameters we are able to assess their role and their impact on the effectiveness and efficiency of the algorithm for provisioning and scheduling in uncertain cloud environments.@inproceedings{10.1109/EMPDP.2019.8671564, author = "Calzarossa, Maria Carla and Massari, Luisa and Nebbione, Giuseppe and Della Vedova, Marco L. and Tessera, Daniele", address = "Pavia, Italy", title = "Tuning {Genetic} {Algorithms} for {Resource} {Provisioning} and {Scheduling} in {Uncertain} {Cloud} {Environments}: {Challenges} and {Findings}", isbn = "978-1-7281-1644-0", shorttitle = "Tuning {Genetic} {Algorithms} for {Resource} {Provisioning} and {Scheduling} in {Uncertain} {Cloud} {Environments}", url = "https://ieeexplore.ieee.org/document/8671564/", doi = "10.1109/EMPDP.2019.8671564", booktitle = "2019 27th {Euromicro} {International} {Conference} on {Parallel}, {Distributed} and {Network}-{Based} {Processing} ({PDP})", publisher = "IEEE", month = "February", year = "2019", pages = "174--180" } -
x
2019
Identifying Fake News from Twitter Sharing Data: A Large-Scale Study
R. Agrawal, L. de Alfaro, G. Ballarin, S. Moret, M. Di Pierro, E. Tacchini, M. L. Della Vedova
arXiv, February 2019.
Social networks offer a ready channel for fake and misleading news to spread and exert influence. This paper examines the performance of different reputation algorithms when applied to a large and statistically significant portion of the news that are spread via Twitter. Our main result is that simple crowdsourcing-based algorithms are able to identify a large portion of fake or misleading news, while incurring only very low false positive rates for mainstream websites. We believe that these algorithms can be used as the basis of practical, large-scale systems for indicating to consumers which news sites deserve careful scrutiny and skepticism.@misc{10.48550/arXiv.1902.07207, author = "Agrawal, Rakshit and de Alfaro, Luca and Ballarin, Gabriele and Moret, Stefano and Di Pierro, Massimo and Tacchini, Eugenio and Della Vedova, Marco L.", title = "Identifying {Fake} {News} from {Twitter} {Sharing} {Data}: {A} {Large}-{Scale} {Study}", shorttitle = "Identifying {Fake} {News} from {Twitter} {Sharing} {Data}", url = "http://arxiv.org/abs/1902.07207", doi = "10.48550/arXiv.1902.07207", urldate = "2024-01-17", publisher = "arXiv", month = "February", year = "2019", keywords = "Computer Science - Social and Information Networks, Computer Science - Machine Learning, Statistics - Machine Learning" } -
B
2019
A Methodological Approach for Time Series Analysis and Forecasting of Web Dynamics
M. C. Calzarossa, M. L. Della Vedova, L. Massari, G. Nebbione, D. Tessera
In N. T. Nguyen, R. Kowalczyk, F. Xhafa, editors, Transactions on Computational Collective Intelligence XXXIII, volume 11610 of Lecture Notes in Computer Science, pages 128–143.Springer Berlin Heidelberg, Berlin, Heidelberg, 2019.
doi:10.1007/978-3-662-59540-4_7
The web is a complex information ecosystem that provides a large variety of content changing over time as a consequence of the combined effects of management policies, user interactions and external events. These highly dynamic scenarios challenge technologies dealing with discovery, management and retrieval of web content. In this paper, we address the problem of modeling and predicting web dynamics in the framework of time series analysis and forecasting. We present a general methodological approach that allows the identification of the patterns describing the behavior of the time series, the formulation of suitable models and the use of these models for predicting the future behavior. Moreover, to improve the forecasts, we propose a method for detecting and modeling the spiky patterns that might be present in a time series. To test our methodological approach, we analyze the temporal patterns of page uploads of the Reuters news agency website over one year. We discover that the upload process is characterized by a diurnal behavior and by a much larger number of uploads during weekdays with respect to weekend days. Moreover, we identify several sudden spikes and a daily periodicity. The overall model of the upload process – obtained as a superposition of the models of its individual components – accurately fits the data, including most of the spikes.@incollection{10.1007/978-3-662-59540-4_7, author = "Calzarossa, Maria Carla and Della Vedova, Marco L. and Massari, Luisa and Nebbione, Giuseppe and Tessera, Daniele", editor = "Nguyen, Ngoc Thanh and Kowalczyk, Ryszard and Xhafa, Fatos", address = "Berlin, Heidelberg", series = "Lecture {Notes} in {Computer} {Science}", title = "A {Methodological} {Approach} for {Time} {Series} {Analysis} and {Forecasting} of {Web} {Dynamics}", volume = "11610", isbn = "978-3-662-59539-8 978-3-662-59540-4", language = "en", booktitle = "Transactions on {Computational} {Collective} {Intelligence} {XXXIII}", publisher = "Springer Berlin Heidelberg", year = "2019", doi = "10.1007/978-3-662-59540-4\_7", pages = "128--143" } -
J
2018
Learning Markov Equivalence Classes of Directed Acyclic Graphs: An Objective Bayes Approach
F. Castelletti, G. Consonni, M. L. Della Vedova, S. Peluso
Bayesian Analysis, December 2018.
A Markov equivalence class contains all the Directed Acyclic Graphs (DAGs) encoding the same conditional independencies, and is represented by a Completed Partially Directed Acyclic Graph (CPDAG), also named Essential Graph (EG). We approach the problem of model selection among noncausal sparse Gaussian DAGs by directly scoring EGs, using an objective Bayes method. Specifically, we construct objective priors for model selection based on the Fractional Bayes Factor, leading to a closed form expression for the marginal likelihood of an EG. Next we propose a Markov Chain Monte Carlo (MCMC) strategy to explore the space of EGs using sparsity constraints, and illustrate the performance of our method on simulation studies, as well as on a real dataset. Our method provides a coherent quantification of inferential uncertainty, requires minimal prior specification, and shows to be competitive in learning the structure of the data-generating EG when compared to alternative state-of-the-art algorithms.@article{10.1214/18-BA1101, author = "Castelletti, Federico and Consonni, Guido and Della Vedova, Marco L. and Peluso, Stefano", title = "Learning {Markov} {Equivalence} {Classes} of {Directed} {Acyclic} {Graphs}: {An} {Objective} {Bayes} {Approach}", volume = "13", issn = "1936-0975", shorttitle = "Learning {Markov} {Equivalence} {Classes} of {Directed} {Acyclic} {Graphs}", url = "https://projecteuclid.org/journals/bayesian-analysis/volume-13/issue-4/Learning-Markov-Equivalence-Classes-of-Directed-Acyclic-Graphs--An/10.1214/18-BA1101.full", doi = "10.1214/18-BA1101", number = "4", journal = "Bayesian Analysis", month = "December", year = "2018" } -
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2018
Automatic Online Fake News Detection Combining Content and Social Signals
M. L. Della Vedova, E. Tacchini, S. Moret, G. Ballarin, M. DiPierro, L. de Alfaro
In 2018 22nd Conference of Open Innovations Association (FRUCT), 272–279. Jyvaskyla, May 2018. IEEE.
doi:10.23919/FRUCT.2018.8468301
The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis (i.e. analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern. In this paper, we first propose a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8\%. Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7\%.@inproceedings{10.23919/FRUCT.2018.8468301, author = "Della Vedova, Marco L. and Tacchini, Eugenio and Moret, Stefano and Ballarin, Gabriele and DiPierro, Massimo and de Alfaro, Luca", address = "Jyvaskyla", title = "Automatic {Online} {Fake} {News} {Detection} {Combining} {Content} and {Social} {Signals}", isbn = "978-952-68653-4-8", doi = "10.23919/FRUCT.2018.8468301", booktitle = "2018 22nd {Conference} of {Open} {Innovations} {Association} ({FRUCT})", publisher = "IEEE", month = "May", year = "2018", pages = "272--279" } -
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2017
Adaptive Real-Time Scheduling of Cyber-Physical Energy Systems
D. De Martini, G. Benetti, M. L. Della Vedova, T. Facchinetti
ACM Transactions on Cyber-Physical Systems, 1(4):1–25, October 2017.
This article addresses the application of real-time scheduling to the reduction of the peak load of power consumption generated by electric loads in Cyber-Physical Energy Systems (CPES). The goal is to reduce the peak load while achieving a desired Quality of Service of the physical system under control. The considered physical processes are characterized by integrator dynamics and modelled as sporadic real-time activities. Timing constraints are obtained from physical parameters and are used to manage the activation of electric loads by a real-time scheduling algorithm. As a main contribution, an algorithm derived from the multi-processor real-time scheduling domain is proposed to efficiently deal with a high number of physical processes (i.e., electric loads), making its scalability suitable for large CPES, such as smart energy grids. The cyber-physical nature of the proposed method arises from the tight interaction between the physical processes operated by the electric loads, and the applied scheduling. To allow the use of the proposed approach in practical applications, modelling approximations and uncertainties on physical parameters are explicitly included in the model. An adaptive control strategy is proposed to guarantee the requirements on physical values under control in presence of modelling and measurement uncertainties. The compensation for such uncertainties is done by dynamically adapting the values of timing parameters used by the scheduler. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads. The performance of the method is evaluated by means of physically accurate simulations of thermal systems, showing a remarkable reduction of the peak load and a robust enforcement of the desired physical requirements.@article{10.1145/3047412, author = "De Martini, Daniele and Benetti, Guido and Della Vedova, Marco L. and Facchinetti, Tullio", title = "Adaptive {Real}-{Time} {Scheduling} of {Cyber}-{Physical} {Energy} {Systems}", volume = "1", issn = "2378-962X, 2378-9638", doi = "10.1145/3047412", language = "en", number = "4", journal = "ACM Transactions on Cyber-Physical Systems", month = "October", year = "2017", pages = "1--25" } -
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2017
Peak load optimization through 2-dimensional packing and multi-processor real-time scheduling
D. De Martini, G. Benetti, F. Cipolla, D. Caprino, M. L. Della Vedova, T. Facchinetti
In Proceedings of the Computing Frontiers Conference, 275–278. Siena Italy, May 2017. ACM.
The use of real-time scheduling methods to coordinate a set of power loads is being explored in the field of Cyber-Physical Energy Systems, with the goal of optimizing the aggregated peak load of power used by many electric loads. Real-time scheduling has attractive features in this domain. Thanks to its inherent resource optimization, which limits the number of concurrent tasks that are running at the same time, real-time scheduling provides direct benefits to peak load optimization. This paper shows the combined use of a two-dimensional bin-packing method and an optimal multi-processor real-time scheduling algorithm to coordinate the activation of electric loads. The result is an effective global scheduling approach where the activation of loads is organized into a pattern that takes into account the timing constraints of the loads and the actual combination of active loads. The validation is done by scheduling a set of thermal loads (heaters) in a building, with accurately modeled temperature dynamics. The proposed method is shown to achieve a significant peak load reduction, up to around 70\%, w.r.t. the traditional thermostat controller.@inproceedings{10.1145/3075564.3075587, author = "De Martini, Daniele and Benetti, Guido and Cipolla, Filippo and Caprino, Davide and Della Vedova, Marco L. and Facchinetti, Tullio", address = "Siena Italy", title = "Peak load optimization through 2-dimensional packing and multi-processor real-time scheduling", isbn = "978-1-4503-4487-6", doi = "10.1145/3075564.3075587", language = "en", booktitle = "Proceedings of the {Computing} {Frontiers} {Conference}", publisher = "ACM", month = "May", year = "2017", pages = "275--278" } -
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2017
Some like it Hoax: Automated fake news detection in social networks
E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret, L. de Alfaro
In CEUR Workshop Proceedings, volume 1960. Skopje, 2017.
URL: http://ceur-ws.org/Vol-1960/paper2.pdf
In the recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic online hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who “liked” them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99\% even when the training set contains less than 1\% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.@inproceedings{xxx, author = "Tacchini, Eugenio and Ballarin, Gabriele and Della Vedova, Marco L. and Moret, Stefano and de Alfaro, Luca", address = "Skopje", title = "Some like it {Hoax}: {Automated} fake news detection in social networks", volume = "1960", shorttitle = "Some like it {Hoax}", url = "http://ceur-ws.org/Vol-1960/paper2.pdf", booktitle = "{CEUR} {Workshop} {Proceedings}", year = "2017" } -
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2016
The Economics of Cloud Parallelism under Uncertainty
M. L. Della Vedova, D. Tessera, M. C. Calzarossa, J. Weinman
IEEE Cloud Computing, 3(6):16–22, November 2016.
Parallelism has many advantages in accelerating compute tasks amenable to speed up. However, whereas parallel processing in an elastic, pay-per-use cloud can generate numerous benefits, there's a hidden downside due to the fundamental statistics and interrelationships of tasks whose completion times are stochastic.@article{10.1109/MCC.2016.137, author = "Della Vedova, Marco L. and Tessera, Daniele and Calzarossa, Maria Carla and Weinman, Joe", title = "The {Economics} of {Cloud} {Parallelism} under {Uncertainty}", volume = "3", issn = "2325-6095", doi = "10.1109/MCC.2016.137", number = "6", journal = "IEEE Cloud Computing", month = "November", year = "2016", pages = "16--22" } -
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2016
Probabilistic provisioning and scheduling in uncertain Cloud environments
M. L. Della Vedova, D. Tessera, M. C. Calzarossa
In 2016 IEEE Symposium on Computers and Communication (ISCC), 797–803. Messina, June 2016. IEEE.
Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.@inproceedings{10.1109/ISCC.2016.7543834, author = "Della Vedova, Marco L. and Tessera, Daniele and Calzarossa, Maria Carla", address = "Messina", title = "Probabilistic provisioning and scheduling in uncertain {Cloud} environments", isbn = "978-1-5090-0679-3", doi = "10.1109/ISCC.2016.7543834", booktitle = "2016 {IEEE} {Symposium} on {Computers} and {Communication} ({ISCC})", publisher = "IEEE", month = "June", year = "2016", pages = "797--803" } -
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2016
G. Benetti, D. Caprino, M. L. Della Vedova, T. Facchinetti
Sustainable Cities and Society, 20:124–141, January 2016.
This paper proposes a review of the scientific literature on electric load management (ELM). Relevant topics include the smart grid, demand-side management, demand-response methods, and peak load reduction. The evaluation is performed by a systematic literature review (SLR) and an evaluation of the recent advances in the state of the art. The analysis is based on the classification of 200+ papers, considering the covered topics/problems, assumptions, constraints, and the proposed methods. Statistical results show a growing interest in ELM in the last few years, and a fast obsolescence of older results. A lack of common benchmarking frameworks has been detected.@article{10.1016/j.scs.2015.05.002, author = "Benetti, Guido and Caprino, Davide and Della Vedova, Marco L. and Facchinetti, Tullio", title = "Electric load management approaches for peak load reduction: {A} systematic literature review and state of the art", volume = "20", issn = "22106707", shorttitle = "Electric load management approaches for peak load reduction", doi = "10.1016/j.scs.2015.05.002", language = "en", journal = "Sustainable Cities and Society", month = "January", year = "2016", pages = "124--141" } -
B
2016
M. C. Calzarossa, M. L. Della Vedova, L. Massari, D. Petcu, M. I. M. Tabash, D. Tessera
In L. Fiondella, A. Puliafito, editors, Principles of Performance and Reliability Modeling and Evaluation, Springer Series in Reliability Engineering, pages 525–550.Springer International Publishing, Cham, 2016.
doi:10.1007/978-3-319-30599-8_20
Despite the fast evolution of cloud computing, up to now the characterization of cloud workloads has received little attention. Nevertheless, a deep understanding of their properties and behavior is essential for an effective deployment of cloud technologies and for achieving the desired service levels. While the general principles applied to parallel and distributed systems are still valid, several peculiarities require the attention of both researchers and practitioners. The aim of this chapter is to highlight the most relevant characteristics of cloud workloads as well as identify and discuss the main issues related to their deployment and the gaps that need to be filled.@incollection{10.1007/978-3-319-30599-8_20, author = "Calzarossa, Maria Carla and Della Vedova, Marco L. and Massari, Luisa and Petcu, Dana and Tabash, Momin I. M. and Tessera, Daniele", editor = "Fiondella, Lance and Puliafito, Antonio", address = "Cham", series = "Springer {Series} in {Reliability} {Engineering}", title = "Workloads in the {Clouds}", isbn = "978-3-319-30597-4 978-3-319-30599-8", booktitle = "Principles of {Performance} and {Reliability} {Modeling} and {Evaluation}", publisher = "Springer International Publishing", year = "2016", doi = "10.1007/978-3-319-30599-8\_20", pages = "525--550" } -
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2015
Applying limited-preemptive scheduling to peak load reduction in smart buildings
D. Caprino, M. L. Della Vedova, T. Facchinetti
In 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), 1–8. Luxembourg, Luxembourg, September 2015. IEEE.
The coordination of appliances in a smart building to limit the peak load is one of the common objectives of power load management approaches such as the Demand-Side Management (DSM). The DSM, in turn, is an important research challenge in the field of smart energy systems and smart grids. This paper investigates the use of the limited-preemption scheduling approach to the coordination of a set of household appliances in a smart building. This approach is enabled by the application of a real-time scheduling framework to manage the activation of electric loads. The limited-preemption technique aims to reduce the number of stop/restart operations applied to interruptible devices, while ensuring the same performance in terms of peak load reduction. The original contribution of this paper w.r.t. to previous works on real-time scheduling with limited-preemption is to present some peculiar issues related to preemptions of electric loads and to assess suitability and benefits of this approach when applied to interruptible household appliances. Simulated results show the effectiveness of this method.@inproceedings{10.1109/ETFA.2015.7301454, author = "Caprino, Davide and Della Vedova, Marco L. and Facchinetti, Tullio", address = "Luxembourg, Luxembourg", title = "Applying limited-preemptive scheduling to peak load reduction in smart buildings", isbn = "978-1-4673-7929-8", doi = "10.1109/ETFA.2015.7301454", booktitle = "2015 {IEEE} 20th {Conference} on {Emerging} {Technologies} \\& {Factory} {Automation} ({ETFA})", publisher = "IEEE", month = "September", year = "2015", pages = "1--8" } -
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2014
Peak shaving through real-time scheduling of household appliances
D. Caprino, M. L. Della Vedova, T. Facchinetti
Energy and Buildings, 75:133–148, June 2014.
doi:10.1016/j.enbuild.2014.02.013
The problem of limiting the peak load of the power consumed by a set of electric loads has been largely addressed in over 5 decades of research on power systems. The motivation of such attention arises from the benefits that a smoother load profile brings to the management of power systems. This paper illustrates an approach to the peak shaving problem that leverages the real-time scheduling discipline to coordinate the activation/deactivation of a set of loads. The real-time scheduling is an active research topic in the field of computing systems. The innovative idea proposed in this paper is to apply existing real-time scheduling algorithms and analysis methods to the management of power loads. This solution requires an adequate modeling of considered devices in order to derive a representation in terms of timing parameters. The modeling approach enables the handling of a set of heterogeneous loads in a coordinated manner. In particular, this paper focuses on the modeling and management of household appliances. For this purpose, a set of the most common appliances is modeled and their activation is controlled by the proposed scheduling policy. Realistic assumptions are made on the daily usage of each device. The derived results show an effective and predicable reduction of the peak load while guaranteeing the user comfort associated with the load operation. The peak load of a single apartment is reduced by the 8\% in the average case and by the 41\% w.r.t. the worst-case. Considering the coalition of several apartments, the scheduling approach achieves a peak load reduction up to 46\%.@article{10.1016/j.enbuild.2014.02.013, author = "Caprino, Davide and Della Vedova, Marco L. and Facchinetti, Tullio", title = "Peak shaving through real-time scheduling of household appliances", volume = "75", issn = "03787788", doi = "10.1016/j.enbuild.2014.02.013", language = "en", journal = "Energy and Buildings", month = "June", year = "2014", pages = "133--148" } -
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2014
Modeling and real-time control of an industrial air multi-compressor system
T. Facchinetti, G. Benetti, M. L. Della Vedova
In Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014), 67–76. Pisa, June 2014. IEEE.
This paper presents a control algorithm for an air multi-compressor system. The goal is to achieve adequate performance in terms of air pressure regulation by properly coordinating a set of compressors driven by fixed speed motors. The coordination is required to impose an upper bound to the activation frequency of electric drives. A multi-compressor system is intended to be a viable alternative to compressor systems based on Variable Speed Drives (VSD) operated by inverters, which suffer of several technical and economic drawbacks. The control strategy is based on the evaluation of the timing associated to activations/deactivations of each compressor. Such evaluation is determined by the values of physical variables that determine the system behavior, including air flows, pressures and temperature. The periodic measurement of the actual pressure is performed to dynamically adjust the estimation of relevant time instants in case of variations of working conditions. The algorithm takes into account the dynamics of the air pressure, as well as timing constraints on the minimum period between two subsequent activations of each compressor. The effectiveness of the multi-compressor solution is evaluated by simulation.@inproceedings{10.1109/SIES.2014.6871189, author = "Facchinetti, Tullio and Benetti, Guido and Della Vedova, Marco L.", address = "Pisa", title = "Modeling and real-time control of an industrial air multi-compressor system", isbn = "978-1-4799-4023-3", doi = "10.1109/SIES.2014.6871189", booktitle = "Proceedings of the 9th {IEEE} {International} {Symposium} on {Industrial} {Embedded} {Systems} ({SIES} 2014)", publisher = "IEEE", month = "June", year = "2014", pages = "67--76" } -
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2013
Real-Time Physical Systems and Electric Load Scheduling
M. L. Della Vedova
PhD Thesis, Università di Pavia, June 2013.
@phdthesis{xxx, author = "Della Vedova, Marco L.", address = "Pavia, Italy", type = "{PhD} {Thesis}", title = "Real-{Time} {Physical} {Systems} and {Electric} {Load} {Scheduling}", school = "Università di Pavia", month = "June", year = "2013" } -
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2013
Real-Time Scheduling for Peak Load Reduction in a Large Set of HVAC Loads
M. L. Della Vedova, T. Facchinetti
In ENERGY 2013, The Third International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, 161–166. Lisbon, Portugal, March 2013. IARIA.
This paper presents a technique to predictably coordinate the activation of Heating, Ventilation and Air Conditioning systems (HVACs) in order to limit the overall peak load of power consumption (peak shaving). The proposed solution represents a viable approach to the Demand-Side Management in the context of a smart grid for this type of loads. The coordination method performs a load shifting based on the discipline of real-time scheduling traditionally studied in the field of computing systems. With this approach, individual constraints on the temperature associated with the activation of each HVAC can be satisfied. The main advantage of the proposed technique is its low computational complexity, which allows to manage large sets of loads. A specific approach is proposed and evaluated to deal with large sets of loads by properly partitioning the load set into sub-sets (scheduling groups) that are scheduled independently from each other. Simulation results based on realistic parameters show that the peak load can be reduced by 35\% in normal working conditions, and up to 60\% with respect to worst case situations, without affecting the comfort achieved by each HVAC.@inproceedings{xxx, author = "Della Vedova, Marco L. and Facchinetti, Tullio", address = "Lisbon, Portugal", title = "Real-{Time} {Scheduling} for {Peak} {Load} {Reduction} in a {Large} {Set} of {HVAC} {Loads}", isbn = "978-1-61208-259-2", url = "https://goo.gl/JH3ooQ", booktitle = "{ENERGY} 2013, {The} {Third} {International} {Conference} on {Smart} {Grids}, {Green} {Communications} and {IT} {Energy}-aware {Technologies}", publisher = "IARIA", month = "March", year = "2013", keywords = "Real-time systems, Scheduling, Demand-Side Management, Load shifting, Power system control", pages = "161--166" } -
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2012
Feedback scheduling of real-time physical systems with integrator dynamics
M. L. Della Vedova, T. Facchinetti
In Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012), 1–8. Krakow, Poland, September 2012. IEEE.
This paper addresses the application of real-time scheduling for the reduction of the peak load of power consumption generated by electric loads in a power system. The considered physical processes are characterized by integrator dynamics and modeled as sporadic real-time activities. To enable the applicability in realistic scenarios, modeling approximations and uncertainties on physical parameters are explicitly included in the model. A feedback control strategy is proposed to guarantee the requirements on physical values under control in presence of modeling and measurement uncertainties. To compensate for such uncertainties, the value of timing parameters used by the scheduler are dynamically adapted. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads.@inproceedings{10.1109/ETFA.2012.6489581, author = "Della Vedova, Marco L. and Facchinetti, Tullio", address = "Krakow, Poland", title = "Feedback scheduling of real-time physical systems with integrator dynamics", isbn = "978-1-4673-4737-2 978-1-4673-4735-8 978-1-4673-4736-5", doi = "10.1109/ETFA.2012.6489581", booktitle = "Proceedings of 2012 {IEEE} 17th {International} {Conference} on {Emerging} {Technologies} \\& {Factory} {Automation} ({ETFA} 2012)", publisher = "IEEE", month = "September", year = "2012", pages = "1--8" } -
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2012
Real-time scheduling for industrial load management
M. L. Della Vedova, T. Facchinetti
In 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), 707–713. Florence, Italy, September 2012. IEEE.
doi:10.1109/EnergyCon.2012.6348243
A new approach based on real-time scheduling has been recently proposed for the management of electric loads composing a power system with the goal to reduce the peak load of power consumption. The novel idea is to model the power system in terms of timing parameters traditionally adopted in the field of real-time computing systems. In this way, the activation/deactivation of devices can be managed using classical scheduling algorithms developed for executing processing tasks in real-time systems. To prove the effectiveness of the proposed methodology, this paper shows its application to an industrial plant. Electric loads are modeled using timing parameters and a real-time scheduler is used to coordinate their activation. The coordinated management achieves the peak load reduction while meeting given constraints on the industrial process under control. The paper presents the modeling approach of the industrial process. The behavior of the control method is assessed by a simulated example.@inproceedings{10.1109/EnergyCon.2012.6348243, author = "Della Vedova, Marco L. and Facchinetti, Tullio", address = "Florence, Italy", title = "Real-time scheduling for industrial load management", isbn = "978-1-4673-1454-1 978-1-4673-1453-4 978-1-4673-1452-7", doi = "10.1109/EnergyCon.2012.6348243", booktitle = "2012 {IEEE} {International} {Energy} {Conference} and {Exhibition} ({ENERGYCON})", publisher = "IEEE", month = "September", year = "2012", pages = "707--713" } -
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2012
Platooning control of autonomous nonholonomic mobile robots in a human-robot coexisting environment
M. L. Della Vedova, M. Rubagotti, T. Facchinetti, A. Ferrara
In 2012 American Control Conference (ACC), 6569–6574. Montreal, QC, June 2012. IEEE.
This paper proposes a gradient tracking algorithm based on artificial harmonic potential fields, to support the platooning of a team of nonholonomic mobile robots. The main motivation is the need of dynamically changing the goal-point associated with each mobile robot, in order to guarantee the platoon string stability. Mobile obstacles are taken into account with an approach based on the so-called collision cone, and a time-varying artificial security radius is associated with each obstacle, in order to prevent collisions. In addition, the proposed method ensures recovering of the connectivity between robots forming the platoon, after that one of them goes far away from the others and loses the connection during an obstacle avoidance maneuver. Finally, the so-called interference index has been evaluated, to show the low impact of robot motion on human behaviors.@inproceedings{10.1109/ACC.2012.6314938, author = "Della Vedova, Marco L. and Rubagotti, Matteo and Facchinetti, Tullio and Ferrara, Antonella", address = "Montreal, QC", title = "Platooning control of autonomous nonholonomic mobile robots in a human-robot coexisting environment", isbn = "978-1-4577-1096-4 978-1-4577-1095-7 978-1-4577-1094-0 978-1-4673-2102-0", doi = "10.1109/ACC.2012.6314938", booktitle = "2012 {American} {Control} {Conference} ({ACC})", publisher = "IEEE", month = "June", year = "2012", pages = "6569--6574" } -
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2011
Time-optimal sliding-mode control of a mobile robot in a dynamic environment
M. Rubagotti, A. Ferrara, M. L. Della Vedova
IET Control Theory & Applications, 5(16):1916–1924, November 2011.
In this study, an original strategy to control a mobile robot in a dynamic environment is presented. The strategy consists of two main elements. The first is the method for the online trajectory generation based on harmonic potential fields, capable of generating velocity and orientation references, which extends classical results on harmonic potential fields for the case of static environments to the case when the presence of a moving obstacle with unknown motion is considered. The second is the design of sliding-mode controllers capable of making the controlled variables of the robot track in a finite minimum time both the velocity and the orientation references.@article{10.1049/iet-cta.2010.0678, author = "Rubagotti, Matteo and Ferrara, Antonella and Della Vedova, Marco L.", title = "Time-optimal sliding-mode control of a mobile robot in a dynamic environment", volume = "5", issn = "1751-8644, 1751-8652", doi = "10.1049/iet-cta.2010.0678", language = "en", number = "16", journal = "IET Control Theory \\& Applications", month = "November", year = "2011", pages = "1916--1924" } -
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2011
Real-Time Modeling for Direct Load Control in Cyber-Physical Power Systems
T. Facchinetti, M. L. Della Vedova
IEEE Transactions on Industrial Informatics, 7(4):689–698, November 2011.
This paper presents an innovative approach to use real-time scheduling techniques for the automation of electric loads in Cyber-Physical Power Systems. The goal is to balance the electric power usage to achieve an optimized upper bound on the power peak load, while guaranteeing specific constraints on the physical process controlled by the electric loads. Timing parameters derived from the scheduling discipline of real-time computing systems are used to model electric devices. Real-time scheduling algorithms can be exploited to achieve the upper bound by predictably and timely switching on/off the devices composing the electrical system. The paper shows the relevance of electric load balancing in power systems to motivate the use of real-time techniques to achieve predictability of electric loads scheduling. Real-Time Physical Systems (RTPS) are introduced as a novel modeling methodology of a physical system based on real-time parameters. They enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. The model of the physical process considered in this work is characterized by uncertainties that are compensated by a suitable feedback control policy, based on the dynamic adaptation of real-time parameter values. A number of relevant relationships between real-time and physical parameters are derived.@article{10.1109/TII.2011.2166787, author = "Facchinetti, Tullio and Della Vedova, Marco L.", title = "Real-{Time} {Modeling} for {Direct} {Load} {Control} in {Cyber}-{Physical} {Power} {Systems}", volume = "7", issn = "1551-3203, 1941-0050", doi = "10.1109/TII.2011.2166787", number = "4", journal = "IEEE Transactions on Industrial Informatics", month = "November", year = "2011", pages = "689--698" } -
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2011
Electric loads as Real-Time tasks: An application of Real-Time Physical Systems
M. L. Della Vedova, E. Di Palma, T. Facchinetti
In 2011 7th International Wireless Communications and Mobile Computing Conference, 1117–1123. Istanbul, Turkey, July 2011. IEEE.
doi:10.1109/IWCMC.2011.5982697
This paper describes the application of Real-Time Physical Systems (RTPS) as a novel approach to model the physical process of Cyber-Physical Systems (CPS), with specific focus on Cyber-Physical Energy Systems (CPES). The proposed approach is based on the real-time scheduling theory which is nowadays developed to manage concurrent computing tasks on processing platforms. Therefore, the physical process is modeled in terms of real-time parameters and timing constraints, so that real-time scheduling algorithms can be applied to manage the timely allocation of resources. The advantage is to leverage the strong mathematical background of real-time systems in order to achieve predictability and timing correctness on the physical process behind the considered CPS. The paper provides an introduction to the possible application of RTPS to energy systems. The analogy between real-time computing systems and energy systems is presented; moreover, the relationship between RTPS and related research fields is traced. Finally, the introduced techniques are proposed to optimize the peak load of power consumption in electric power systems. This method is suitable for systems spanning from small networks to smart grids.@inproceedings{10.1109/IWCMC.2011.5982697, author = "Della Vedova, Marco L. and Di Palma, Ettore and Facchinetti, Tullio", address = "Istanbul, Turkey", title = "Electric loads as {Real}-{Time} tasks: {An} application of {Real}-{Time} {Physical} {Systems}", isbn = "978-1-4244-9539-9", shorttitle = "Electric loads as {Real}-{Time} tasks", doi = "10.1109/IWCMC.2011.5982697", booktitle = "2011 7th {International} {Wireless} {Communications} and {Mobile} {Computing} {Conference}", publisher = "IEEE", month = "July", year = "2011", pages = "1117--1123" } -
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2011
Right Sizing Customer Care: An Approach for Sustainable Service Level Agreements
T. Barroero, G. Motta, M. L. Della Vedova
In 2011 International Joint Conference on Service Sciences, 40–43. Taipei, May 2011. IEEE.
Call centers operational models size staff by balancing service quality and efficiency. Classic models consider only the case of dedicated staff. We propose a model for right sizing shared staff, thus extending the classical Erlang C by using the Gamma incomplete function. This model has a wider coverage and support flexible configurations as required by emerging markets and, furthermore, it enables transparent pricing against a certified service level.@inproceedings{10.1109/IJCSS.2011.16, author = "Barroero, Thiago and Motta, Gianmario and Della Vedova, Marco L.", address = "Taipei", title = "Right {Sizing} {Customer} {Care}: {An} {Approach} for {Sustainable} {Service} {Level} {Agreements}", isbn = "978-1-4577-0326-3", shorttitle = "Right {Sizing} {Customer} {Care}", doi = "10.1109/IJCSS.2011.16", booktitle = "2011 {International} {Joint} {Conference} on {Service} {Sciences}", publisher = "IEEE", month = "May", year = "2011", pages = "40--43" } -
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2010
M. L. Della Vedova, M. Ruggeri, T. Facchinetti
In Proceedings of the 18th International Conference on Real-Time and Network Systems (RTNS), 41–49. Toulouse, France, November 2010.
URL: https://hal.inria.fr/RTNS2010/hal-00544477
This paper introduces a class of real-time systems denoted as Real-Time Physical Systems (RTPS), in which a physical quantity of interest is associated with a real-time resource. The physical quantity behavior is determined by scheduling events generated by a real-time scheduling algorithm. RTPS systems aim to generalize some existing models used in real-time computing systems, namely power-aware and temperature-aware systems. Moreover, they have been conceived to put a bridge across real-time systems and the rapidly growing research field of Cyber-Physical Systems. In this paper we focus on a specific physical system where a state variable changes with an exponential decay behavior and the associated real-time resource must be scheduled in order to bound the value of the state variable within the desired working range. The aim is to determine the relationship between those physical and real-time parameters. For this purpose interesting properties are highlighted and relevant results are derived regarding the considered system model.@inproceedings{xxx, author = "Della Vedova, Marco L. and Ruggeri, Michele and Facchinetti, Tullio", address = "Toulouse, France", title = "On real-time physical systems", url = "https://hal.inria.fr/RTNS2010/hal-00544477", booktitle = "Proceedings of the 18th {International} {Conference} on {Real}-{Time} and {Network} {Systems} ({RTNS})", month = "November", year = "2010", pages = "41--49" } -
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2010
Real-Time Modeling and Control of a Cyber-Physical Energy System
T. Facchinetti, M. L. Della Vedova
In Energy Aware Design and Analysis of Cyber Physical Systems (WEA-CPS), 2010 First International Workshop on. April 2010.
@inproceedings{xxx, author = "Facchinetti, Tullio and Della Vedova, Marco L.", title = "Real-{Time} {Modeling} and {Control} of a {Cyber}-{Physical} {Energy} {System}", booktitle = "Energy {Aware} {Design} and {Analysis} of {Cyber} {Physical} {Systems} ({WEA}-{CPS}), 2010 {First} {International} {Workshop} on", month = "April", year = "2010" } -
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2009
Real-time platooning of mobile robots: design and implementation
M. L. Della Vedova, T. Facchinetti, A. Ferrara, A. Martinelli
In 2009 IEEE Conference on Emerging Technologies & Factory Automation, 1–4. Palma de Mallorca, Spain, September 2009. IEEE.
The platooning is a coordination technique for teams of mobile units that aims at letting each unit to move closely to its preceding neighbour, thus forming the so-called platoon. This paper describes the design and implementation of a distributed robotics application where a team of autonomous mobile robots are coordinated to move as a platoon. The focus will be on the on-board real-time computing that allows a predictable robot's behavior. Experimental results are shown to assess the performance of the proposed platform.@inproceedings{10.1109/ETFA.2009.5347246, author = "Della Vedova, Marco L. and Facchinetti, Tullio and Ferrara, Antonella and Martinelli, Alessandro", address = "Palma de Mallorca, Spain", title = "Real-time platooning of mobile robots: design and implementation", isbn = "978-1-4244-2727-7", shorttitle = "Real-time platooning of mobile robots", doi = "10.1109/ETFA.2009.5347246", booktitle = "2009 {IEEE} {Conference} on {Emerging} {Technologies} \\& {Factory} {Automation}", publisher = "IEEE", month = "September", year = "2009", pages = "1--4" } -
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2009
Visual Interaction for Real-Time Navigation of Autonomous Mobile Robots
M. L. Della Vedova, T. Facchinetti, A. Ferrara, A. Martinelli
In 2009 International Conference on CyberWorlds, 211–218. Bradford, September 2009. IEEE.
Visual feedback is one of the most adopted solutions for driving the navigation of autonomous robots in unknown environments. This paper presents the structure of a visual interaction system suitable for real-time robotics applications. By means of a specific modeling, the visual system allows a team of mobile robots to perform any relevant visual task in a timely fashion. As a matter of fact, the guarantee of real-time constraints for the processing tasks related with the visual feedback is crucial to achieve an accurate and robust control of mobile robots. The proposed visual infrastructure is based on a single camera, which provides a global view of the robot's workspace. A degenerated camera model is developed to allow a planar motion in R 3 . The model simplifies the visual system calibration, while reducing the cost of coordinates transforms between the real-world and the image space during the system operation. To show the behaviour and to derive the performances of the visual interaction system, experimental results are carried out considering the real-time navigation of autonomous mobile robots.@inproceedings{10.1109/CW.2009.24, author = "Della Vedova, Marco L. and Facchinetti, Tullio and Ferrara, Antonella and Martinelli, Alessandro", address = "Bradford", title = "Visual {Interaction} for {Real}-{Time} {Navigation} of {Autonomous} {Mobile} {Robots}", isbn = "978-1-4244-4864-7", doi = "10.1109/CW.2009.24", booktitle = "2009 {International} {Conference} on {CyberWorlds}", publisher = "IEEE", month = "September", year = "2009", pages = "211--218" }