Browsing by Author "Alviano, Mario"

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    Arsenic Ore Mixture Froth Image Generation with Neural Networks and a Language for Declarative Data Validation
    (Università della Calabria, 2022-04-14) Zamayla, Arnel; Greco, Gianluigi; Alviano, Mario; Dodaro, Carmine
    Computer vision systems that measure froth flow velocities and stability designed for flotation froth image analysis are well established in industry, as they are used to control material recovery. However flotation systems that has limited data has not been explored in the same fashion bearing the fact that big data tools like deep convolutional neural networks require huge amounts of data. This lead to the motivation of the research reported in the first part of this thesis, which is to generate synthetic images from limited data in order to create a froth image dataset. The image synthesis is possible through the use of generative adversarial network. The performance of human experts in this domain in identifying the original and synthesized froth images were then compared with the performance of the models. The models exhibited better accuracy levels by average on the tests that were performed. The trained classifier was also compared with some of the established neural network models in the literature like the AlexNet, VGG16 ang ResNet34. Transfer learning was used as a method for this purpose. It also showed that these pretrained networks that are readily available have better accuracy by average comapared to trained experts. The second part of this thesis reports on a language designed for data validation in the context of knowledge representation and reasoning. Specifically, the target language is Answer Set Programming (ASP), a logic-based programming language widely adopted for combinatorial search and optimization, which however lacks constructs for data validation. The language presented in this thesis fulfills this gap by introducing specific constructs for common validation criteria, and also supports the integration of consolidated validation libraries written in Python. Moreover, the language is designed so to inject data validation in ordinary ASP programs, so to promote fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid.
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    Dynamic magic sets
    (2010) Alviano, Mario; Lceone, Nicola; Faber, Wolfgang
    Disjunctive Datalog with stable model semantics is a rule–based language for knowledge representation and common sense reasoning that also allows to use queries for checking the presence of specific atoms in stable models. Expressive- ness is a strength of the language, which indeed captures the second level of the polynomial hierarchy. However, because of this high expressive power, evalu- ating Disjunctive Datalog programs and queries is inherently nondeterministic. In fact, Disjunctive Datalog computations are typically characterized by two distinct phases. The first phase, referred to as program instantiation, is deter- ministic and associates input programs with equivalent ground programs; only deterministic knowledge is inferred in this phase. The second phase, referred to as stable model search, is nondeterministic and computes stable models of instantiated programs. Many query optimization techniques have been proposed in the literature. Among them are Magic Sets, originally introduced for standard Datalog pro- grams. Program instantiation is sufficient for computing the semantics of stan- dard Datalog programs because only deterministic knowledge can be represented in this case. For this reason, the original Magic Set technique is only focused on the optimization of program instantiation. Dynamic Magic Sets are an exten- sion of the technique that takes into account the nondeterministic knowledge encoded into Disjunctive Datalog programs. In fact, in addition to the standard optimization of program instantiation, Dynamic Magic Sets provide further op- timization potential to the subsequent stable model search. In this thesis, Dynamic Magic Sets are proved to be sound and complete for stratified and super–coherent programs. To this end, a strong relationship between magic atoms and unfounded sets is highlighted. Dynamic Magic Sets are also used for proving decidability of reasoning for a class of programs with uninterpreted function symbols. In particular, it is shown that the application of Dynamic Magic Sets to finitely recursive queries generates finitely ground programs, for which decidability of reasoning has been established in the liter- ature. Dynamic Magic Sets have been implemented in a prototype extending DLV, a state–of–the–art system for Disjunctive Datalog programs and queries. The effectiveness of Dynamic Magic Sets has been assessed by experimenting with the prototype system. Experimental results confirm that Dynamic Magic Sets can provide significant, possibly exponential, performance gains.