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W niniejszym artykule zaprezentowano dość śmiałą tezę, że Kodeks pracy nie stanowi obowiązku wyodrębnienia wynagrodzenia zasadniczego dla wszystkich pracowników. W celu uzasadnienia powyższego stanowiska dokonano analizy de lege lata przepisów regulacji kodeksowej dotyczących wynagrodzenia zasadniczego pracowników sfery prywatnej. Zadanie to było utrudnione, gdyż Kodeks pracy wobec pracowników z sektora prywatnego nie odnosi się wprost do wynagrodzenia zasadniczego. Dlatego też w pierwszej kolejności rozważania zmierzają do ustalenia miejsca występowania tytułowego wynagrodzenia w regulacji kodeksowej. Dalej dociekania koncentrują się wokół źródeł ustalania wynagrodzenia zasadniczego, wreszcie analizy miejsca i pozycji, jakie tytułowe wynagrodzenie zajmuje w strukturze wynagrodzenia za pracę.

Achieving a superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural network (CNN) architectures for capturing complex spatial patterns. This result was, to a great extent, due to several analogies between Go board states and 2-D images that CNNs have been designed for, in particular, translational invariance and a relatively large board. In this paper, we verify whether CNN-based move predictors prove effective for Othello , a game with significantly different characteristics, including a much smaller board size and complete lack of translational invariance. We compare several CNN architectures and board encodings, augment them with state-of-the-art extensions, train on an extensive database of experts’ moves, and examine them with respect to move prediction accuracy and playing strength. The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. The best CNNs not only surpass all other 1-ply Othello players proposed to date but defeat (2 ply) Edax, the best open-source Othello player.

Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

The Act on Enforcement Proceedings in Administration contains a legal definition of the term „remuneration”, moreover, it uses the phrases „remuneration from the employment relationship”, „execution from remuneration for work”. Considering the widespread use of an enforcement measure in the form of remuneration for work by administrative enforcement authorities, the question arises whether the Act uses the expression „remuneration” in the same sense as in the above phrases. The article is an attempt to seek answers to the above questions. For this purpose, first of all, the axis of research was related to the determination of the meaning of the term „remuneration” within the meaning of the provisions of the Act. Further research was devoted to a comparative comparison of the established meaning of the term „remuneration” with the phrases „remuneration from the employment relationship”, „execution from remuneration for work”. The set objectives determine the applied research formal-dogmatic method, which should contribute to answering the question.

In test-based problems, solutions produced by search algorithms are typically assessed using average outcomes of interactions with multiple tests. This aggregation leads to information loss, which can render different solutions apparently indifferent and hinder comparison of search algorithms. In this paper we introduce the performance profile, a generic, domain-independent, multi-criteria performance evaluation method that mitigates this problem by characterizing the performance of a solution by a vector of outcomes of interactions with tests of various difficulty. To demonstrate the usefulness of this gauge, we employ it to analyze the behavior of Othello and Iterated Prisoner’s Dilemma players produced by five (co)evolutionary algorithms as well as players known from previous publications. Performance profiles reveal interesting differences between the players, which escape the attention of the scalar performance measure of the expected utility. In particular, they allow us to observe that evolution with random sampling produces players coping well against the mediocre opponents, while the coevolutionary and temporal difference learning strategies play better against the high-grade opponents. We postulate that performance profiles improve our understanding of characteristics of search algorithms applied to arbitrary test-based problems, and can prospectively help design better methods for interactive domains.