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We examine the complexity of inference in Bayesian networks specified by logical languages. We consider representations that range from fragments of propositional logic to function-free first-order logic with equality; in doing so we cover a variety of plate models and of probabilistic relational models. We study the complexity of inferences when network, query and domain are the input (the inferential and the combined complexity), when the network is fixed and query and domain are the input (the query / data complexity), and when the network and query are fixed and the domain is the input (the domain complexity). We draw connections with probabilistic databases and liftability results, and obtain complexity classes that range from polynomial to exponential levels; we identify new languages with tractable inference, and we relate our results to languages based on plates and probabilistic relational models. [ABSTRACT FROM AUTHOR]

This paper introduces a pipeline that assembles a dataset of metocean conditions consisting of wind, wave, and surface currents, and then clusters these data to find the characteristic environmental conditions of each region on the Brazilian coast and the associated Exclusive Economic Zone. Clustering uses the Partitioning Around Medoids algorithm with the silhouette coefficient. As examples, we first present an analysis of the whole Exclusive Economic Zone and then a focused analysis around the Santos port in Southeastern Brazil. [ABSTRACT FROM AUTHOR]

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-Computer interaction and pattern recognition: facial expression recognition and face detection. [ABSTRACT FROM AUTHOR]

There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian networks, while Relational Bayesian Networks (RBNs) employ first-order probability formulas with the same purpose. We examine the coherence checking problem for those formalisms; that is, the problem of guaranteeing that any grounding of a well-formed set of sentences does produce a valid Bayesian network. This is a novel version of de Finetti’s problem of coherence checking for probabilistic assessments. We show how to reduce the coherence checking problem in relational Bayesian networks to a validity problem in first-order logic augmented with a transitive closure operator and how to combine this logic-based approach with faster, but incomplete algorithms. [ABSTRACT FROM AUTHOR]

Contributing to research on digital platform labor in the Global South, this research surveyed 149 Brazilian workers in the Amazon Mechanical Turk (AMT) platform. We begin by offering a demographic overview of the Brazilian turkers and their relation with work in general. In line with previous studies of turkers in the USA and India, AMT offers poor working conditions for Brazilian turkers. Other findings we discuss include: how a large amount of the respondents affirmed they have been formally unemployed for a long period of time; the relative importance of the pay they receive to their financial subsistence; and how Brazilian turkers cannot receive their pay directly into their bank accounts due to Amazon restrictions, making them resort to creative circumventions of the system. Importantly, these "ghost workers" (Gray & Suri, 2019) find ways to support each other and self-organize through the WhatsApp group, where they also mobilize to fight for changes on the platform. As this type of work is still in formation in Brazil, and potentially will grow in the coming years, we argue this is a matter of concern. [ABSTRACT FROM AUTHOR]

Comments on the research articles published by the June 2000 issue of the 'International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.' Key issues and topics explored by the research articles; Author credits; Theoretical significance to uncertainty, fuzziness and knowledge-based systems.

Useful information for offshore systems can be obtained by monitoring sea state parameters, but the accurate estimation of the on-site wave condition is challenging. This paper deals with motion-based wave estimation using neural networks. The main novelty of this contribution is the evaluation of the performance of the data-driven inference system with motion data from different draft conditions. The study is based on the simulated motions of a Floating Production Storage and Offloading (FPSO) unit with multiple loadings, subjected to typical sea conditions observed offshore the Brazilian coast. The inference models were trained to estimate two independent sets of wave related parameters, corresponding to modal wave statistics, which were post processed to identify single and double-peaked sea states. Results showed that estimation errors were quite similar along the evaluated drafts, with an overall good agreement between estimations and reference values. However, a limitation of the method regarding less frequent sea conditions was observed, even when significant wave response is induced. Also, an analysis of the robustness of the estimation models with respect to a draft variation of ± 0.5 m was carried out considering the global wave statistics, presenting small average deviations compared to the expected values. • Ship motion-based wave inference is performed with neural network models. • Crossed sea states are considered by estimating two sets of sea spectrum parameters. • Models derived from data of different vessel drafts present similar performance. • The inference models are robust for a variation of ± 0.5 m in the draft condition. • Less frequent sea conditions may present challenges to the data-driven estimation. [ABSTRACT FROM AUTHOR]

Spatial knowledge plays an essential role in human reasoning, permitting tasks such as locating objects in the world (including oneself), reasoning about everyday actions and describing perceptual information. This is also the case in the field of mobile robotics, where one of the most basic (and essential) tasks is the autonomous determination of the pose of a robot with respect to a map, given its perception of the environment. This is the problem of robot self-localisation (or simply the localisation problem). This paper presents a probabilistic algorithm for robot self-localisation that is based on a topological map constructed from the observation of spatial occlusion. Distinct locations on the map are defined by means of a classical formalism for qualitative spatial reasoning, whose base definitions are closer to the human categorisation of space than traditional, numerical, localisation procedures. The approach herein proposed was systematically evaluated through experiments using a mobile robot equipped with a RGB-D sensor. The results obtained show that the localisation algorithm is successful in locating the robot in qualitatively distinct regions. [ABSTRACT FROM PUBLISHER]