Marx’s concept of distributive justice: an exercise in the formal modeling of political principles

This paper presents an exercise in the formalization of political principles, by taking as its theme the concept of distributive justice that Karl Marx advanced in his Critique of the Gotha Programme. We first summarize the content of the Critique of the Gotha Programme. Next, we transcribe the core of Marx’s presentation of the concept of distributive justice. Following, we present our formalization of Marx’s conception. Then, we make use of that formal analysis to confront Marx’s principle of distributive justice with John Rawls’ conception of justice as fairness, and the principles of distributive justice that derive from it. Finally, we discuss methodological issues relative to, and implications of, the way of formalizing political principles introduced here.     

Encoding Cryptographic Primitives in a Calculus with Polyadic Synchronisation

We thoroughly study the behavioural theory of epi, a ??-calculus extended with polyadic synchronisation. We show that the natural contextual equivalence, barbed congruence, coincides with early bisimilarity, which is thus its co-inductive characterisation. Moreover, we relate early bisimilarity with the other usual notions, ground, late and open, obtaining a lattice of equivalence relations that clarifies the relationship among the ??standard?? bisimilarities. Furthermore, we apply the theory developed to obtain an expressiveness result: epi extended with key encryption primitives may be fully abstractly encoded in the original epi calculus. The proposed encoding is sound and complete with respect to barbed congruence; hence, cryptographic epi (crypto-epi) gets behavioural theory for free, which contrasts with other process languages with cryptographic constructs that usually require a big effort to develop such theory. Therefore, it is possible to use crypto-epi to analyse and to verify properties of security protocols using equational reasoning. To illustrate this claim, we prove compliance with symmetric and asymmetric cryptographic system laws, and the correctness of a protocol of secure message exchange.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

Evolutionary robustness analysis for multi-objective optimization: benchmark problems

This paper presents a new approach to robustness analysis in multi-objective optimization problems aimed at obtaining the most robust Pareto front solutions and distributing the solutions along the most robust regions of the optimal Pareto set. A new set of test problems accounting for the different types of robustness cases is presented in this study. Non-dominated solutions are classified according to their degree of robustness and are distributed along the Pareto front according to specific algorithm parameter values. Verification of the proposed method is carried out using the developed test problems and artificial and real world benchmark test problems present in the literature.     

Dissimilarity-based classification of chromatographic profiles

This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10% for the four-class problem.     

How you move reveals who you are: understanding human behavior by analyzing trajectory data

The widespread use of mobile devices is producing a huge amount of trajectory data, making the discovery of movement patterns possible, which are crucial for understanding human behavior. Significant advances have been made with regard to knowledge discovery, but the process now needs to be extended bearing in mind the emerging field of behavior informatics. This paper describes the formalization of a semantic-enriched KDD process for supporting meaningful pattern interpretations of human behavior. Our approach is based on the integration of inductive reasoning (movement pattern discovery) and deductive reasoning (human behavior inference). We describe the implemented Athena system, which supports such a process, along with the experimental results on two different application domains related to traffic and recreation management.     

A distributed requirements management framework for legal compliance and accountability

Increasingly, new regulations are governing organizations and their information systems. Individuals responsible for ensuring legal compliance and accountability currently lack sufficient guidance and support to manage their legal obligations within relevant information systems. While software controls provide assurances that business processes adhere to specific requirements, such as those derived from government regulations, there is little support to manage these requirements and their relationships to various policies and regulations. We propose a requirements management framework that enables executives, business managers, software developers and auditors to distribute legal obligations across business units and/or personnel with different roles and technical capabilities. This framework improves accountability by integrating traceability throughout the policy and requirements lifecycle. We illustrate the framework within the context of a concrete healthcare scenario in which obligations incurred from the Health Insurance Portability and Accountability Act (HIPAA) are delegated and refined into software requirements. Additionally, we show how auditing mechanisms can be integrated into the framework and how auditors can certify that specific chains of delegation and refinement decisions comply with government regulations.