The Factors of Constitutional Support for the Rule of Law in the System of Public Authorities

The article examines the factors that ensure the constitutional provision of the rule of law in the system of public authorities. The aim of this study was to analyse the factors that ensure the constitutional provision of the rule of law in the system of public authorities. The constitutional design provides for the creation of rational structures for the functioning of public authorities, which should ensure democratic standards, economic development, anti-corruption policy by implementing the principles of the rule of law. The study used data on indicators that describe the state of the rule of law (Rule of Law Index), democracy (Democracy Index) and corruption (Corruption Perceptions Index). Methods of graphical comparison, scattering diagrams, classification of countries by categories were used. A graphical model of the dependence of the rule of law on the development of democracy and perceptions of corruption for 25 European countries is built on the basis of these factors. It is proved that the studied indicators are dependent: countries with a high Rule of Law Index (high level of restrictions on the powers of government institutions, protection of fundamental rights, law enforcement, security) have a high Corruption Perceptions Index (high level of anti-corruption) and Democracy Index. It is concluded that it is necessary to develop the constitutional provision of the rule of law by strengthening democratic values, improving economic growth and competitiveness, increasing control over corruption. Further research should analyse the impact of rule of law factors in low- and middle-income countries.

Probabilistic Graphical Model for Continuous Variables

Most of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

Bayesian Causal Network for Discrete Variables

Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.

Constructing a graphical model of the relationship between physical activity and cognitive function based on a systematic review of prospective evidence

Elucidating the factors that contribute to healthy ageing is an important research goal. Physical activity (PA) has been associated with benefits for cognitive function (CF). However, most of this evidence comes from longitudinal cohort studies which, in the absence of experimental design, have limited scope to make causal inferences regarding observed relationships. This review aimed to utilise recent methodological developments allowing researchers to formulate and answer stronger causal questions using observational data, by following a best-practice method for synthesising evidence to produce a graphical causal model known as a directed acyclic graph (DAG). Following a search of 3 databases (EMBASE, MEDLINE and PsycINFO), 21 observational studies on the PA-CF relationship were reviewed and their methodological quality, characteristics, and key findings were summarised. The outcomes of interest were the covariates and modelling practices employed in each study. The reported covariates were synthesised against a set of criteria to determine their role in the DAG as confounders or mediators of the PA-CF relationship. Every included study had some areas of methodological weakness. The resulting DAG included a wide range of biopsychosocial covariates spanning the entire life-course and indicated potential intermediate pathways between PA and CF via structural brain health. Strengths, limitations and implications of this review for modelling decisions are discussed, prior to the model being taken forward to inform an empirical analysis using data from the UK Biobank cohort, separate from this review.

Chapter 8. A Constraint-Based Approach to Learning and Reasoning

Neural-symbolic models bridge the gap between sub-symbolic and symbolic approaches, both of which have significant limitations. Sub-symbolic approaches, like neural networks, require a large amount of labeled data to be successful, whereas symbolic approaches, like logic reasoners, require a small amount of prior domain knowledge but do not easily scale to large collections of data. This chapter presents a general approach to integrate learning and reasoning that is based on the translation of the available prior knowledge into an undirected graphical model. Potentials on the graphical model are designed to accommodate dependencies among random variables by means of a set of trainable functions, like those computed by neural networks. The resulting neural-symbolic framework can effectively leverage the training data, when available, while exploiting high-level logic reasoning in a certain domain of discourse. Although exact inference is intractable within this model, different tractable models can be derived by making different assumptions. In particular, three models are presented in this chapter: Semantic-Based Regularization, Deep Logic Models and Relational Neural Machines. Semantic-Based Regularization is a scalable neural-symbolic model, that does not adapt the parameters of the reasoner, under the assumption that the provided prior knowledge is correct and must be exactly satisfied. Deep Logic Models preserve the scalability of Semantic-Based Regularization, while providing a flexible exploitation of logic knowledge by co-training the parameters of the reasoner during the learning procedure. Finally, Relational Neural Machines provide the fundamental advantages of perfectly replicating the effectiveness of training from supervised data of standard deep architectures, and of preserving the same generality and expressive power of Markov Logic Networks, when considering pure reasoning on symbolic data. The bonding between learning and reasoning is very general as any (deep) learner can be adopted, and any output structure expressed via First-Order Logic can be integrated. However, exact inference within a Relational Neural Machine is still intractable, and different factorizations are discussed to increase the scalability of the approach.

Gap-com: General model selection criterion for sparse undirected gene networks with non-trivial community structure

We introduce a new model selection criterion for sparse complex gene network modeling where gene co-expression relationships are estimated from data. This is a novel formulation of the gap statistic and it can be used for the optimal choice of a regularization parameter in graphical models. Our criterion favors gene network structure which differs from a trivial gene interaction structure obtained totally at random. We call the criterion the gap-com statistic (gap community statistic). The idea of the gap-com statistic is to examine the difference between the observed and the expected counts of communities (clusters) where the expected counts are evaluated using either data permutations or reference graph (the Erdős-Rényi graph) resampling. The latter represents a trivial gene network structure determined by chance. We put emphasis on complex network inference because the structure of gene networks is usually non-trivial. For example, some of the genes can be clustered together or some genes can be hub genes. We evaluate the performance of the gap-com statistic in graphical model selection and compare its performance to some existing methods using simulated and real biological data example.

A Probabilistic Trust Model for Cloud Services Using Bayesian Networks

With the rapid adoption of cloud computing in the industry, there has been a significant challenge in managing trust between cloud service providers and service consumers. In fact, trust management in cloud computing has become very challenging given the urgent need for cloud service requesters to choose efficient, trustworthy and non-risky services. One of the most important factors that can be considered in the trust or distrust of a service by the applicant is the different quality of services related to the service. Therefore, approaches are needed to assess the trustworthiness of cloud services with respect to the values ​​of their Quality of Service (QoS). Given the uncertainty that exists for cloud services, it is more realistic to model their QoS parameters as random variables and also consider different dependencies between them. In this paper, a new trust model for cloud services is proposed using Bayesian networks. Bayesian network is a probabilistic graphical model that can be used as one of the best methods to control uncertainty. Using Bayesian network makes it possible to infer more accurate QoS values ​​will which leads to the selection of highly trustworthy services by several cloud service requesters. The results of the experiments show that the proposed trust model is highly accurate and significantly reduces the estimation error.