Ahstract- The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known.

In this paper, the application of different machine learning strategies for current- and vibration-based detection of bearing faults in squirrel-cage induction motors is studied. This study compares several feature extraction strategies such as a statistical and spectral analysis of vibration, a statistical analysis of the Hilbert's Transform envelope of vibration, an analysis of the currents deviation to a perfect sinusoid and a statistical and spectral analysis of the Park's Vector Modulus, with its performances being evaluated with the Support Vector Machine, Artificial Neural Network, Random Forests and Extreme Gradient Boosting algorithms. A comparison of results obtained using sampling frequencies of 0.8 kHz, 1 kHz, 2 kHz, 5 kHz and 10 kHz and analysis periods between 20 ms and 100 ms is made and promising models are achieved even with the lowest sampling frequencies.

We present a principled framework for modular Web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For legal MWeb modular rule bases S , the MWebAS and MWebWFS semantics of each rule base s ∈ S with respect to S are defined model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (1) local semantics and different points of view, (2) local closed-world and open-world assumptions, (3) scoped negation-as-failure, (4) restricted propagation of local inconsistencies, and (5) monotonicity of reasoning, for fully shared predicates.

The Extended Resource Description Framework has been proposed to equip RDF graphs with weak and strong negation, as well as derivation rules, increasing the expressiveness of ordinary RDF graphs. In parallel, the Modular Web framework enables collaborative and controlled reasoning in the Semantic Web. In this paper we show how to use the Modular Web Framework to capture the modular semantics for ERDF graphs, supporting local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure.

Unlike monotone single-valued functions, multivalued mappings may have zero, one, or (possibly infinitely) many minimal fixed-points. The contribution of this work is twofold. First, we overview and investigate the existence and computation of minimal fixed-points of multivalued mappings, whose domain is a complete lattice and whose range is its power set. Second, we show how these results are applied to a general form of logic programs, where the truth space is a complete lattice. We show that a multivalued operator can be defined whose fixed-points are in one-to-one correspondence with the models of the logic program.

ERDF stable model semantics is a recently proposed semantics for ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs. Unfortunately, ERDF stable model semantics is in general undecidable. In this paper, we elaborate on the computability and complexity issues of the ERDF stable model semantics.

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

Residuated logic programs have shown to be a generalisation of a number of approaches to logic programming under uncertain or vague information, including fuzzy or annotated or probabilistic or similarity-based logic programming frameworks. Various computational approaches have been developed for propositional residuated logic programs: on the one hand, there exists a bottom-up neural-like implementation of the fixed-point semantics which calculates the successive iterations of the immediate consequences operator; on the other hand, a goal-oriented top-down tabulation procedure has recently been introduced. In this paper, we introduce a sound and complete tabulation-based proof procedure for the first-order extension of residuated logic programs.