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Paolo Mignone
Assistant Professor presso Università degli Studi di Bari
Paolo Mignone is an accomplished professional with a background in informatics.
He studied Bachelors in Informatics at Università degli Studi di Bari.
Paolo Mignone has an impressive academic and professional history, having worked at various renowned organizations.
He was formerly an Assistant Professor, Graduate Research Fellow, and PhD Student at Università degli Studi di Bari.
Additionally, he served as a Visiting Researcher at Jozef Stefan Institute, showcasing his expertise in research.
Paolo Mignone has also worked in the industry as a Software Developer at Auriga - The banking e-volution, Web Developer at MC Srl, and Java Consultant at ThinkOpen Srl.
His contributions to the field of informatics can be explored further on his institutional website, ResearchGate profile, and ORCID account.
Highlights
Open Access publication on the prestigious journal of Machine Learning! TITLE: Distributed and explainable GHSOM for anomaly detection in sensor networks AUTHORS: Paolo Mignone, Roberto Corizzo & Michelangelo Ceci ABSTRACT: The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by limitations such as the inability to analyze large-scale data, a reduced effectiveness when data presents multiple densities, a strong dependence on user-defined threshold configurations, and a lack of explainability in the extracted predictions. In this paper, we propose a distributed deep learning method that extends growing hierarchical self-organizing maps, originally designed for clustering tasks, to address anomaly detection tasks. The SOM-based modeling capabilities of the method enable the analysis of data with multiple densities, by exploiting multiple SOMs organized as a hierarchy. Our map-reduce implementation under Apache Spark allows the method to process and analyze large-scale sensor network data. An automatic threshold-tuning strategy reduces user efforts and increases the robustness of the method with respect to noisy instances. Moreover, an explainability component resorting to instance-based feature ranking emphasizes the most salient features influencing the decisions of the anomaly detection model, supporting users in their understanding of raised alerts. Experiments are conducted on five real-world sensor network datasets, including wind and photovoltaic energy production, vehicular traffic, and pedestrian flows. Our results show that the proposed method outperforms state-of-the-art anomaly detection competitors. Furthermore, a scalability analysis reveals that the method is able to scale linearly as the data volume presented increases, leveraging multiple worker nodes in a distributed computing setting. Qualitative analyses on the level of anomalous pollen in the air further emphasize the effectiveness of our proposed method, and its potential in determining the level of danger in raised alerts. KEYWORDS: Anomalous activities, Sensor networks, Predictive models, Distributed deep learning
The 11th edition of the Workshop New Frontiers in Mining Complex Patterns (NFMCP), in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), will be held in Turin, Italy, from the 18th to the 22nd of September 2023. The workshop aims to bring together researchers and practitioners of data mining and machine learning interested in analysing complex data.
Call for Papers We welcome submissions focusing on recent advances and the latest developments in analysing complex and massive data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams. In addition, we encourage submissions from statistics, machine learning and big data analytics, which present techniques that take advantage of the informative richness of complex, massive data for efficiently and effectively identifying new patterns.
Submissions discussing and introducing new algorithmic foundations and representation formalisms in complex pattern discovery and submissions describing preliminary, promising studies and recently accepted papers are also welcome.
The solutions can be rooted in different research fields, such as Pattern Mining, Neural Networks and Deep Learning, Probabilistic Inference, Decision Trees, Rule Learning, and Reinforcement Learning.
Dates Workshop Paper Submission Deadline: 12th June 2023 Workshop Paper Author Notification: 12th July 2023 Workshop Date: 18th September 2023
More Information Workshop URL: https://lnkd.in/dTQA9qBj Website URL: https://2023.ecmlpkdd.org
Contact If you have any queries, please contact the organization committee at: [email protected]
#machinelearning #datamining #artificialintelligence #ai #bigdata #datascience
