RESULTS FROM 1 TO 7 OF 7

2023, Articolo in rivista, ENG

Promoting Geographical Competences in Primary School through Active Learning and Digital Storytelling. Results of a Study from Genoa, Italy

Manganello F; Rampulla M

In this contribution, the role of active learning and digital technologies, namely Digital Storytelling, in promoting geographical competences within primary school education is explored. A study is presented, conducted in the specific setting of a fourth-grade primary class in Genoa, a metropolitan city in the Italian region of Liguria, exemplifying the application of this educational approach. The theoretical foundations for this study encompass three key pillars: preserving the epistemological and content-based features of Geography in primary education, recognizing the value of pedagogical dimensions and strategies in developing geographical competences, and leveraging digital technologies, including non-specialized ones, to support the design of learning activities that employ innovative methods and exploit the potentials of narrative techniques. Within this framework, Digital Storytelling is critically evaluated for its capacity to stimulate fruitful learning dynamics, and to encourage the development of geographical, creative, and digital competences. The findings of the study highlighted that the active and creative use of Digital Storytelling markedly enriched Geography education. The implemented approach contributed enhance geographical competences related to pupils' understanding and articulation of their place in the world, while also fostering a positive attitude towards the subject.

J-ReADInG, Journal of research and didactics in geography (Online) 2 (12), pp. 83–99

DOI: 10.4458/6083-07

2022, Contributo in atti di convegno, ENG

Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery

Francesco Folino, Gianluigi Folino, Massimo Guarascio, Luigi Pontieri

Detecting deviant traces in business process logs is a crucial task in modern organizations due to the detrimental effect of certain deviant behaviors (e.g., attacks, frauds, faults). Training a Deviance Detection Model (DDM)only over labeled traces with supervised learning methods unfits real-life contexts where a small fraction of the traces are labeled. Thus, we here propose an Active-Learning-based approach to discovering a deep DDM ensemble that exploits a temporal ensembling method to train and fuse multiple DDMs sharing the same DNN architecture, devised in a way ensuring rapid convergence in relatively few training epochs. Experts' supervision is required only on small numbers of unlabelled traces exhibiting high values of (epistemic) prediction uncertainty, estimated in an ensemble-driven fashion. Tests on real data confirmed the approach's effectiveness, even compared to the results obtained by state-of-the-art supervised methods in the ideal case where all the data are labeled.

International Symposium on Methodologies for Intelligent Systems (ISMIS 2022), Cosenza, Italy, 3-5 Ottobre 2022

2022, Articolo in rivista, ENG

Boosting Cyber-Threat Intelligence via Collaborative Intrusion Detection

Guarascio, Massimo; Cassavia, Nunziato; Pisani, Francesco Sergio; Manco, Giuseppe

Sharing threat events and Indicators of Compromise (IoCs) enables quick and crucial decision making relative to effective countermeasures against cyberattacks. However, the current threat information sharing solutions do not allow easy communication and knowledge sharing among threat detection systems (in particular Intrusion Detection Systems (IDS)) exploiting Machine Learning (ML) techniques. Moreover, the interaction with the expert, which represents an important component to gather verified and reliable input data for the ML algorithms, is weakly supported. To address all these issues, ORISHA, a platform for ORchestrated Information SHaring and Awareness enabling the cooperation among threat detection systems and other information awareness components, is proposed here. ORISHA is backed by a distributed Threat Intelligence Platform based on a network of interconnected Malware Information Sharing Platform instances, which enables the communication with several Threat Detection layers belonging to different organizations. Within this ecosystem, Threat Detection Systems mutually benefit by sharing knowledge that allows them to refine the underlying predictive accuracy. Uncertain cases, i.e. examples with low anomaly scores, are proposed to the expert, who acts with the role of oracle in an Active Learning scheme. By interfacing with a honeynet, ORISHA allows for enriching the knowledge base with further positive attack instances and then yielding robust detection models. An experimentation conducted on a well-known Intrusion Detection benchmark demonstrates the validity of the proposed architecture.

Future generation computer systems 135, pp. 30–43

DOI: 10.1016/j.future.2022.04.028

2020, Contributo in atti di convegno, ENG

ARLectio: An Augmented Reality Platform to Support Teachers in Producing Educational Resources

Farella, M.; Arrigo, M.; Taibi, D.; Todaro, G.; Chiazzese, G. and Fulantelli, G.

In this article we present a learning platform named 'ARLectio' based on Augmented Reality (AR) technology that aims at supporting teachers in promoting AR experience at school. The ARLectio platform has been developed in the framework of the FabLab SchoolNet project, funded by the European Commission under the Erasmus+ programme. With the use of Augmented Reality, Educational robotics and 3D printing, the objective of the project is to develop a new learning model based on the design and implementation of "objects" that can promote creativity and innovation, communication, collaboration, critical thinking and computational thinking skills in students.

12th International Conference on Computer Supported Education, 02/05/2020,04/05/2020

DOI: 10.5220/0009579104690475

2020, Contributo in atti di convegno, ENG

Active Learning-based Classification in Automated Connected Vehicles

Alaa Awad Abdellatif, Carla Fabiana Chiasserini, Francesco Malandrino

Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challenge, however, is to collect and select real-time and reliable information for the correct classification of unexpected, and often rare, situations that may happen on the road. Indeed, the data generated by vehicles, or received from neighboring vehicles, may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. In particular, given the available information, our solution selects the data to add to the training set by trading off between two essential features, namely, quality and diversity. The results, obtained using real-world data sets, show that the proposed method significantly outperforms state-of-the-art solutions, providing high classification accuracy at the cost of a limited bandwidth requirement for the data exchange between vehicles.

IEEE INFOCOM PERSIST-IoT workshop on Pervasive Systems in the IoT era, 2020

2019, Presentazione, ENG

Teaching Practices for Active Learning

Loredana Le Pera

An overview of teaching practices with a focus on active learning strategies as a possible alternative to traditional lecturing.

Excellence in Learning and Teaching Biochemistry a FEBS Education Meeting, Rome, Italy, 16-17/09/2019

2013, Contributo in volume, ENG

Domain Adaptation for Dependency Parsing at EVALITA 2011

F. Dell'Orletta and S. Marchi and S. Montemagni and G. Venturi and T. Agnoloni and E. Francesconi

The domain adaptation task was aimed at investigating techniques for adapting state-of-the-art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target do- main, namely the legal domain, represent two main novelties of the task organised at Evalita 2011 with respect to previous domain adaptation ini- tiatives. In this paper, we define the task and describe how the datasets were created from different resources. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results.

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Keyword

Active Learning

RESULTS FROM 1 TO 7 OF 7