2004
Conference article
Metadata Only Access
Scheduling and load balancing
Luque E, Castaños Jg, Markatos Ep, Perego R
Scheduling and Load Balancing techniques are key issues for the performance of applications executed in parallel and distributed environments, and for the efficient utilization of these computational resources. Research in this field has a long history and is well consolidated. Nevertheless, the evolution of parallel and distributed systems toward clusters, computational grids, and global computing environments, introduces new challenging problems that require a new generation of scheduling and load balancing algorithms. Topic 3 in Euro-Par 2004 covers all aspects related to scheduling and load balancing from application and system levels, to theoretical foundations and practical tools. All these aspects are addressed by contributed papers.
Luque E, Castaños Jg, Markatos Ep, Perego R
Scheduling and Load Balancing techniques are key issues for the performance of applications executed in parallel and distributed environments, and for the efficient utilization of these computational resources. Research in this field has a long history and is well consolidated. Nevertheless, the evolution of parallel and distributed systems toward clusters, computational grids, and global computing environments, introduces new challenging problems that require a new generation of scheduling and load balancing algorithms. Topic 3 in Euro-Par 2004 covers all aspects related to scheduling and load balancing from application and system levels, to theoretical foundations and practical tools. All these aspects are addressed by contributed papers.
See at:
CNR IRIS 
2010
Conference article
Open Access
Scheduling and load balancing. Introduction to topic
Yahyapour R, Perego R, Desprez F, Epstein L, Bernat F G
Scheduling and load balancing techniques are crucial for implementing efficient parallel and distributed applications and for making best use of parallel and distributed systems. This includes planning and optimization of resource allocation as well as coping with the dynamics of the systems.DOI: 10.1007/978-3-642-15277-1_15
Metrics:
Yahyapour R, Perego R, Desprez F, Epstein L, Bernat F G
Scheduling and load balancing techniques are crucial for implementing efficient parallel and distributed applications and for making best use of parallel and distributed systems. This includes planning and optimization of resource allocation as well as coping with the dynamics of the systems.DOI: 10.1007/978-3-642-15277-1_15
Metrics:
See at:
link.springer.com 
| doi.org | CNR IRIS | CNR IRIS | link.springer.com
2004
Conference article
Restricted
Statistical properties of transactional databases
Palmerini P, Orlando S, Perego R
Most of the complexity of common data mining tasks is due to the unknown amount of information contained in the data being mined. The more patterns and corelations are contained in such data, the more resources are needed to extract them. This is confirmed by the fact that in general there is not a single best algorithm for a given data mining task on any possible kind of input dataset. Rather, in order to achieve good performances, strategies and optimizations have to be adopted according to the dataset specific characteristics. For example one typical distinction in transactional databases is between sparse and dense datasets. In this paper we consider Frequent Set Counting as a case study for data mining algorithms. We propose a statistical analysis of the properties of transactional datasets that allows for a characterization of the dataset complexity. We show how such characterization can be used in many fields, from performance prediction to optimization.
Palmerini P, Orlando S, Perego R
Most of the complexity of common data mining tasks is due to the unknown amount of information contained in the data being mined. The more patterns and corelations are contained in such data, the more resources are needed to extract them. This is confirmed by the fact that in general there is not a single best algorithm for a given data mining task on any possible kind of input dataset. Rather, in order to achieve good performances, strategies and optimizations have to be adopted according to the dataset specific characteristics. For example one typical distinction in transactional databases is between sparse and dense datasets. In this paper we consider Frequent Set Counting as a case study for data mining algorithms. We propose a statistical analysis of the properties of transactional datasets that allows for a characterization of the dataset complexity. We show how such characterization can be used in many fields, from performance prediction to optimization.
See at:
CNR IRIS | CNR IRIS | portal.acm.org
2006
Conference article
Restricted
Mining frequent closed itemsets out-of-core
Lucchese C, Orlando S, Perego R
Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far. Since also the result set of all the frequent itemsets is likely to be undesirably large, condensed representations, such as closed itemsets, have recently gained a lot of attention. In this paper we discuss the limitations of the partitioning techniques adopted by external memory algorithms for extracting all the frequent itemsets, when applied to closed itemsets mining. The main issue is that the closedness of an itemset cannot be evaluated only using the local knowledge available in a single partition of the input dataset. A further step is thus needed to correctly merge the partial results. We introduce the first algorithm for mining closed itemsets out of core. The algorithm exploits a divide-et-impera approach, where the input dataset is split into smaller partitions, such that not only they can be loaded, but also they can be mined entirely into the main memory. Moreover, we devised a simple technique based on a new theoretical result that allows us to reduce the problem of merging partial solutions to an external memory sorting problem.
Lucchese C, Orlando S, Perego R
Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far. Since also the result set of all the frequent itemsets is likely to be undesirably large, condensed representations, such as closed itemsets, have recently gained a lot of attention. In this paper we discuss the limitations of the partitioning techniques adopted by external memory algorithms for extracting all the frequent itemsets, when applied to closed itemsets mining. The main issue is that the closedness of an itemset cannot be evaluated only using the local knowledge available in a single partition of the input dataset. A further step is thus needed to correctly merge the partial results. We introduce the first algorithm for mining closed itemsets out of core. The algorithm exploits a divide-et-impera approach, where the input dataset is split into smaller partitions, such that not only they can be loaded, but also they can be mined entirely into the main memory. Moreover, we devised a simple technique based on a new theoretical result that allows us to reduce the problem of merging partial solutions to an external memory sorting problem.
See at:
CNR IRIS | CNR IRIS | www.siam.org
2001
Conference article
Restricted
Enhancing the apriori algorithm for frequent set counting
Orlando S, Palmerini P, Perego R
In this paper we propose DCP, a new algorithm for solv- ing the Frequent Set Counting problem, which enhances Apriori. Our goal was to optimize the initial iterations of Apriori, i.e. the most time consuming ones when datasets characterized by short or medium length frequent patterns are considered. The main improvements regard the use of an innovative method for storing candidate set of items and counting their support, and the exploitation of eective pruning techniques which signicantly reduce the size of the dataset as execution progresses.
Orlando S, Palmerini P, Perego R
In this paper we propose DCP, a new algorithm for solv- ing the Frequent Set Counting problem, which enhances Apriori. Our goal was to optimize the initial iterations of Apriori, i.e. the most time consuming ones when datasets characterized by short or medium length frequent patterns are considered. The main improvements regard the use of an innovative method for storing candidate set of items and counting their support, and the exploitation of eective pruning techniques which signicantly reduce the size of the dataset as execution progresses.
2017
Book
Open Access
Proceedings of the 8th Italian Information Retrieval Workshop
Crestani F, Di Noia T, Perego R
This volume contains the papers presented at IIR'17: 8th Italian Information Retrieval Workshop held on June 05-07, 2017 in Lugano, Switzerland. The purpose of the Italian Information Retrieval (IIR) workshop series is to provide a forum for stimulating and disseminating research in information retrieval, where Italian researchers (especially young ones) and researchers a liated with Italian institutions can network and discuss their research results in an informal way. Previously IIR workshops took place in Venice (2016), Cagliari (2015), Rome (2014), Pisa (2013), Bari (2012), Milan (2011) and Padua (2010).Source: CEUR WORKSHOP PROCEEDINGS
Crestani F, Di Noia T, Perego R
This volume contains the papers presented at IIR'17: 8th Italian Information Retrieval Workshop held on June 05-07, 2017 in Lugano, Switzerland. The purpose of the Italian Information Retrieval (IIR) workshop series is to provide a forum for stimulating and disseminating research in information retrieval, where Italian researchers (especially young ones) and researchers a liated with Italian institutions can network and discuss their research results in an informal way. Previously IIR workshops took place in Venice (2016), Cagliari (2015), Rome (2014), Pisa (2013), Bari (2012), Milan (2011) and Padua (2010).Source: CEUR WORKSHOP PROCEEDINGS
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
1994
Other
Open Access
Ottimizzazione parallela con simulated annealing e calcolo speculativo
Perego R
Simulated Annealing (SA) is a Monte Carlo search technique for obtaining approximate solutions to combinatorial problems depending from independent variables with many degrees of freedom. In this paper a new parallel SA algorithm which exploits the technique of speculative computation is presented. The proposal sensibly shortens the high computational costs of traditional SA implementations without violating its serial decision sequence. Our implementation is similar to that proposed by Witte, Chamberlain e Franklin [9], althoug characterized by a higher flexibility and by an enhanced dynamic behavior which allows the efficient exploitation of more parallelism. Results achieved on a nCUBE 2 multicomputer running our parallel SA algorithm applied to a VLSI placement and routing problem are reported and analyzed.
Perego R
Simulated Annealing (SA) is a Monte Carlo search technique for obtaining approximate solutions to combinatorial problems depending from independent variables with many degrees of freedom. In this paper a new parallel SA algorithm which exploits the technique of speculative computation is presented. The proposal sensibly shortens the high computational costs of traditional SA implementations without violating its serial decision sequence. Our implementation is similar to that proposed by Witte, Chamberlain e Franklin [9], althoug characterized by a higher flexibility and by an enhanced dynamic behavior which allows the efficient exploitation of more parallelism. Results achieved on a nCUBE 2 multicomputer running our parallel SA algorithm applied to a VLSI placement and routing problem are reported and analyzed.
1994
Other
Open Access
Ottimizzazione parallela con simulated annealing e calcolo speculativo
Perego R
Simulated Annealing (SA) is a Monte Carlo search technique for obtaining approximate solutions to combinatorial problems depending from independent variables with many degrees of freedom. In this paper a new parallel SA algorithm which exploits the technique of speculative computation is presented. The proposal sensibly shortens the high computational costs of traditional SA implementations without violating its serial decision sequence. Our implementation is similar to that proposed by Witte, Chamberlain e Franklin [9], althoug characterized by a higher flexibility and by an enhanced dynamic behavior which allows the efficient exploitation of more parallelism. Results achieved on a nCUBE 2 multicomputer running our parallel SA algorithm applied to a VLSI placement and routing problem are reported and analyzed.
Perego R
Simulated Annealing (SA) is a Monte Carlo search technique for obtaining approximate solutions to combinatorial problems depending from independent variables with many degrees of freedom. In this paper a new parallel SA algorithm which exploits the technique of speculative computation is presented. The proposal sensibly shortens the high computational costs of traditional SA implementations without violating its serial decision sequence. Our implementation is similar to that proposed by Witte, Chamberlain e Franklin [9], althoug characterized by a higher flexibility and by an enhanced dynamic behavior which allows the efficient exploitation of more parallelism. Results achieved on a nCUBE 2 multicomputer running our parallel SA algorithm applied to a VLSI placement and routing problem are reported and analyzed.
1994
Other
Open Access
A new mapping heuristic for last generation multicomputers
Perego R, De Petris G
An abstract is not available.
Perego R, De Petris G
An abstract is not available.
1994
Other
Open Access
Locality-based programming on a reconfigurable transputer network
Perego R
An abstract is not available.
Perego R
An abstract is not available.
2018
Conference article
Open Access
Continuation methods and curriculum learning for learning to rank
Ferro N, Lucchese C, Maistro M, Perego R
In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank. The basic idea is to design the training process as a learning path across increasingly complex training instances and objective functions. We propose to instantiate continuation methods in Learning to Rank by changing the IR measure to optimize during training, and we present two different curriculum learning strategies to identify easy training examples. Experimental results show that simple continuation methods are more promising than curriculum learning ones since they allow for slightly improving the performance of state-of-the-art ?-MART models and provide a faster convergence speed.DOI: 10.1145/3269206.3269239
Metrics:
Ferro N, Lucchese C, Maistro M, Perego R
In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank. The basic idea is to design the training process as a learning path across increasingly complex training instances and objective functions. We propose to instantiate continuation methods in Learning to Rank by changing the IR measure to optimize during training, and we present two different curriculum learning strategies to identify easy training examples. Experimental results show that simple continuation methods are more promising than curriculum learning ones since they allow for slightly improving the performance of state-of-the-art ?-MART models and provide a faster convergence speed.DOI: 10.1145/3269206.3269239
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
Boosting learning to rank with user dynamics and continuation methods
Ferro N, Lucchese C, Maistro M, Perego R
Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.Source: INFORMATION RETRIEVAL (BOSTON), vol. 23 (issue 6), pp. 528-554
DOI: 10.1007/s10791-019-09366-9
Metrics:
Ferro N, Lucchese C, Maistro M, Perego R
Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.Source: INFORMATION RETRIEVAL (BOSTON), vol. 23 (issue 6), pp. 528-554
DOI: 10.1007/s10791-019-09366-9
Metrics:
See at:
Copenhagen University Research Information System | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | CNR IRIS | Information Retrieval | link.springer.com | ISTI Repository | Information Retrieval | CNR IRIS | CNR IRIS
2018
Journal article
Open Access
From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)
Ferro N, Fuhr N, Grefenstette G, Konstan Ja, Castells P, Daly Em, Declerck T, Ekstrand Md, Geyer W, Gonzalo J, Kuflik T, Lind'En K, Magnini B, Nie Jy, Perego R, Shapira B, Soboroff I, Tintarev N, Verspoor K, Willemsen Mc, Zobel J
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of predic- tion models describing the relationship between assumptions, features and resulting performanceSource: DAGSTUHL MANIFESTOS, vol. 7 (issue 1), pp. 96-139
DOI: 10.4230/dagman.7.1.96
Metrics:
Ferro N, Fuhr N, Grefenstette G, Konstan Ja, Castells P, Daly Em, Declerck T, Ekstrand Md, Geyer W, Gonzalo J, Kuflik T, Lind'En K, Magnini B, Nie Jy, Perego R, Shapira B, Soboroff I, Tintarev N, Verspoor K, Willemsen Mc, Zobel J
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of predic- tion models describing the relationship between assumptions, features and resulting performanceSource: DAGSTUHL MANIFESTOS, vol. 7 (issue 1), pp. 96-139
DOI: 10.4230/dagman.7.1.96
Metrics:
See at:
drops.dagstuhl.de | CNR IRIS | ISTI Repository | CNR IRIS
1998
Conference article
Restricted
An MPI-based Run-Time Support to Coordinate HPF Tasks
Orlando S, Perego R
This paper describes COLTHPF, an MPI-based run-time support for the coordination of concurrent and communicating HPF tasks. COLTHPF is conceived for use by a compiler of a high-level coordination language to structure a set of data-parallel HPF tasks according to popular forms of task-parallelism. Since it requires only small changes to the run-time support of the HPF compiler used, COLTHPF is easily portable among different compilation systems. The paper outlines design and implementation issues, and reports the results of experiments conducted on an SGI/Cray T3E.DOI: 10.1007/bfb0056587
Metrics:
Orlando S, Perego R
This paper describes COLTHPF, an MPI-based run-time support for the coordination of concurrent and communicating HPF tasks. COLTHPF is conceived for use by a compiler of a high-level coordination language to structure a set of data-parallel HPF tasks according to popular forms of task-parallelism. Since it requires only small changes to the run-time support of the HPF compiler used, COLTHPF is easily portable among different compilation systems. The paper outlines design and implementation issues, and reports the results of experiments conducted on an SGI/Cray T3E.DOI: 10.1007/bfb0056587
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
1998
Conference article
Restricted
A Coordination Layer for Exploiting Task Parallelism with HPF
Orlando S, Perego R
This paper introduces COLThpf, a run-time support for exploiting task parallelism within HPF programs, which can be employed by a compiler of a high-level coordination language to structure a set of data-parallel HPF tasks according to popular paradigms of task-parallelism. We use COLThpf to program a computer vision application and report the results obtained by running the application on an SGI/Cray T3E.DOI: 10.1007/3-540-49530-4_30
Metrics:
Orlando S, Perego R
This paper introduces COLThpf, a run-time support for exploiting task parallelism within HPF programs, which can be employed by a compiler of a high-level coordination language to structure a set of data-parallel HPF tasks according to popular paradigms of task-parallelism. We use COLThpf to program a computer vision application and report the results obtained by running the application on an SGI/Cray T3E.DOI: 10.1007/3-540-49530-4_30
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
1998
Conference article
Open Access
Scheduling Data-Parallel Computations on Heterogeneous and Time-Shared Environments
Orlando S, Perego R
This paper addresses the problem of load balancing data-parallel computations on heterogeneous and time-shared parallel computing environments, where load imbalance may be introduced by the different capacities of processors populating a computer, or by the sharing of the same computational resources among several users. To solve this problem we propose a run-time support for parallel loops based upon a hybrid (static + dynamic) scheduling strategy. The main features of our technique are the absence of centralization and synchronization points, the prefetching of work toward slower processors, and the overlapping of communication latencies with useful computation.DOI: 10.1007/bfb0057874
Metrics:
Orlando S, Perego R
This paper addresses the problem of load balancing data-parallel computations on heterogeneous and time-shared parallel computing environments, where load imbalance may be introduced by the different capacities of processors populating a computer, or by the sharing of the same computational resources among several users. To solve this problem we propose a run-time support for parallel loops based upon a hybrid (static + dynamic) scheduling strategy. The main features of our technique are the absence of centralization and synchronization points, the prefetching of work toward slower processors, and the overlapping of communication latencies with useful computation.DOI: 10.1007/bfb0057874
Metrics:
See at:
link.springer.com | doi.org | CNR IRIS | CNR IRIS | link.springer.com
1998
Journal article
Restricted
A mapping heuristic for minimizing network contention
Raffaele Perego
The combinatorial optimization problem of assigning tasks of a parallel program to processing nodes (pn's) of a parallel system is a well-known NP-hard problem. In this paper a new greedy heuristic for compile-time mapping of tasks without precedence constraints is proposed. The solution is addressed to modern multicomputers based on k-ary n-cube direct interconnection networks exploiting the e-cube routing algorithm and the wormhole flow control strategy. The proposed algorithm takes into account communication delays due to network blocking of colliding messages. Results achieved on several program-derived graphs with up to 784 tasks demonstrate the effectiveness of the approach followed.Source: JOURNAL OF SYSTEMS ARCHITECTURE, vol. 45 (issue 1), pp. 65-82
DOI: 10.1016/s1383-7621(97)00073-8
Metrics:
Raffaele Perego
The combinatorial optimization problem of assigning tasks of a parallel program to processing nodes (pn's) of a parallel system is a well-known NP-hard problem. In this paper a new greedy heuristic for compile-time mapping of tasks without precedence constraints is proposed. The solution is addressed to modern multicomputers based on k-ary n-cube direct interconnection networks exploiting the e-cube routing algorithm and the wormhole flow control strategy. The proposed algorithm takes into account communication delays due to network blocking of colliding messages. Results achieved on several program-derived graphs with up to 784 tasks demonstrate the effectiveness of the approach followed.Source: JOURNAL OF SYSTEMS ARCHITECTURE, vol. 45 (issue 1), pp. 65-82
DOI: 10.1016/s1383-7621(97)00073-8
Metrics:
See at:
Journal of Systems Architecture | CNR IRIS | CNR IRIS | www.sciencedirect.com | www.scopus.com
1992
Other
Metadata Only Access
OCTOPUS: architettura del sistema e modalita' di utilizzo
Perego R, Pedelini P
Abstract non disponibile
Perego R, Pedelini P
Abstract non disponibile
See at:
CNR IRIS
2021
Conference article
Restricted
Hierarchical dependence-aware evaluation measures for conversational search
Faggioli G, Ferrante M, Ferro N, Perego R, Tonellotto N
Conversational agents are drawing a lot of attention in the information retrieval (IR) community also thanks to the advancements in language understanding enabled by large contextualized language models. IR researchers have long ago recognized the importance o fa sound evaluation of new approaches. Yet, the development of evaluation techniques for conversational search is still an underlooked problem. Currently, most evaluation approaches rely on procedures directly drawn from ad-hoc search evaluation, treating utterances in a conversation as independent events, as if they were just separate topics, instead of accounting for the conversation context. We overcome this issue by proposing a framework for defining evaluation measures that are aware of the conversation context and the utterance semantic dependencies. In particular, we model the conversations as Direct Acyclic Graphs (DAG), where self-explanatory utterances are root nodes, while anaphoric utterances are linked to sentences that contain their missing semantic information. Then,we propose a family of hierarchical dependence-aware aggregations of the evaluation metrics driven by the conversational graph. In our experiments, we show that utterances from the same conversation are 20% more correlated than utterances from different conversations. Thanks to the proposed framework, we are able to include such correlation in our aggregations, and be more accurate when determining which pairs of conversational systems are deemed significantly different.DOI: 10.1145/3404835.3463090
Metrics:
Faggioli G, Ferrante M, Ferro N, Perego R, Tonellotto N
Conversational agents are drawing a lot of attention in the information retrieval (IR) community also thanks to the advancements in language understanding enabled by large contextualized language models. IR researchers have long ago recognized the importance o fa sound evaluation of new approaches. Yet, the development of evaluation techniques for conversational search is still an underlooked problem. Currently, most evaluation approaches rely on procedures directly drawn from ad-hoc search evaluation, treating utterances in a conversation as independent events, as if they were just separate topics, instead of accounting for the conversation context. We overcome this issue by proposing a framework for defining evaluation measures that are aware of the conversation context and the utterance semantic dependencies. In particular, we model the conversations as Direct Acyclic Graphs (DAG), where self-explanatory utterances are root nodes, while anaphoric utterances are linked to sentences that contain their missing semantic information. Then,we propose a family of hierarchical dependence-aware aggregations of the evaluation metrics driven by the conversational graph. In our experiments, we show that utterances from the same conversation are 20% more correlated than utterances from different conversations. Thanks to the proposed framework, we are able to include such correlation in our aggregations, and be more accurate when determining which pairs of conversational systems are deemed significantly different.DOI: 10.1145/3404835.3463090
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS