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This paper deals with the challenging problem of simultaneously integrating interpretablility and reliability into prediction models in machine learning. It proposes to combine the interpretable models of decision rules with the reliable models based on conformal prediction. The result is a new technique of conformal decision rules. Given a test instance, the technique is capable of providing a point prediction, an explanation, and a confidence value for that prediction plus a prediction set. The experiments show when and how conformal decision rules can be used for interpretable and reliable machine learning.

In this work we propose a probabilistic hierarchical generative approach for users' preference data, which is designed to overcome the limitation of current methodologies in Recommender Systems and thus to meet both prediction and recommendation accuracy. The Bayesian Hierarchical User Community Model (BH-UCM) focuses both on modeling the popularity of items and the distribution over item ratings. An extensive evaluation over two popular benchmark datasets shows that the combined modeling of item popularity and rating provides a powerful framework both for rating prediction and for the generation of accurate recommendation lists.

In this work we propose a research foundation for uniformly querying both movement data and patterns extracted from movement data. Our proposal is based on a formal framework, called 2W Model, that defines the knowledge discovery execution process as a progressive combination of mining and querying operators. Furthermore, a query language is proposed that extends SQL in two respects, namely a pattern definition operator and the capability to uniformly manipulate both raw data and unveiled patterns. The resulting Moving-Object data mining query language is implemented in DAEDALUS, an innovative computational engine serving as a query execution layer on top of the Hermes Moving Object Database. A qualitative evaluation of the whole system by means of a concrete case study is presented.

In this work we propose DAEDALUS, a knowledge discovery sys- tem that enables a uniform querying of movement data and patterns. Such sys- tem is founded on a formal framework that defines knowledge discovery exe- cution process as a progressive combination of mining and querying operators. The core component of DAEDALUS is the query language that extends SQL in two respects, namely a pattern definition operator and the capability of uni- formly manipulate both raw data and unveiled patterns. DAEDALUS is specifi- cally focussed on movement data and has been implemented as a query execution layer on top of the Hermes Moving Object Database. A qualitative evaluation of the system will show the power of DAEDALUS in progressive mining task over movement data.

In this paper, we introduce principles and functional architecture of Borè, a novel semantic-based framework that truly realizes Web3.0 principles and tools for the next-generation Internet. In particular, we detail peculiar aspects of Borè, such as architecture, social cooperation and collaborative filtering, which capture relevant innovations due to the proposed framework.

Edge computing is a paradigm in which data is intelligently processed close to its source. Along with advancements in deep learning, there is a growing interest in using deep neural networks at the edge for predictive analytics. Given the realistic constraints in computational resources of edge devices, this combination is challenging. In order to bridge the gap between deep learning models and efficient edge analytics, a container-based framework is presented that evaluates user-specified deep learning models for efficiency on the edge. The proposed framework is validated on a rotating machinery fault diagnosis use case. Conclusions on efficient state-of-the-art models for rotating machine fault diagnosis were drawn and appropriately reported. ispartof: pages:569-581 ispartof: Communications in Computer and Information Science vol:1752 pages:569-581 ispartof: Joint European Conference on Machine Learning and Knowledge Discovery in Databases location:Grenoble, France date:19 Sep - 23 Sep 2022 status: Published online