Information2014, 5(3), 424-439; doi:10.3390/info5030424 - published 21 July 2014 Show/Hide Abstract
Abstract: Biological systems represent a unique class of physical systems in how they process and manage information. This suggests that changes in the flow and distribution of information played a prominent role in the origin of life. Here I review and expand on an emerging conceptual framework suggesting that the origin of life may be identified as a transition in causal structure and information flow, and detail some of the implications for understanding the early stages chemical evolution.
Information2014, 5(3), 404-423; doi:10.3390/info5030404 - published 14 July 2014 Show/Hide Abstract
Abstract: We argue that a critical difference distinguishing machines from organisms and computers from brains is not complexity in a structural sense, but a difference in dynamical organization that is not well accounted for by current complexity measures. We propose a measure of the complexity of a system that is largely orthogonal to computational, information theoretic, or thermodynamic conceptions of structural complexity. What we call a system’s dynamical depth is a separate dimension of system complexity that measures the degree to which it exhibits discrete levels of nonlinear dynamical organization in which successive levels are distinguished by local entropy reduction and constraint generation. A system with greater dynamical depth than another consists of a greater number of such nested dynamical levels. Thus, a mechanical or linear thermodynamic system has less dynamical depth than an inorganic self-organized system, which has less dynamical depth than a living system. Including an assessment of dynamical depth can provide a more precise and systematic account of the fundamental difference between inorganic systems (low dynamical depth) and living systems (high dynamical depth), irrespective of the number of their parts and the causal relations between them.
Information2014, 5(3), 389-403; doi:10.3390/info5030389 - published 8 July 2014 Show/Hide Abstract
Abstract: A consensus is emerging that the multiple forms, functions and properties of information cannot be captured by a simple categorization into classical and quantum information. Similarly, it is unlikely that the applicable physics of information is a single classical discipline, completely expressible in mathematical terms, but rather a complex, multi- and trans-disciplinary field involving deep philosophical questions about the underlying structure of the universe. This paper is an initial attempt to present the fundamental physics of non-quantum information in terms of a novel non-linguistic logic. Originally proposed by the Franco-Romanian thinker Stéphane Lupasco (1900–1988), this logic, grounded in quantum mechanics, can reflect the dual aspects of real processes and their evolution at biological, cognitive and social levels of reality. In my update of this logical system—Logic in Reality (LIR)—a change in perspective is required on the familiar notions in science and philosophy of causality, continuity and discontinuity, time and space. I apply LIR as a critique of current approaches to the physical grounding of information, focusing on its qualitative dualistic aspects at non-quantum levels as a set of physical processes embedded in a physical world.
Information2014, 5(2), 357-388; doi:10.3390/info5020357 - published 11 June 2014 Show/Hide Abstract
Abstract: Information is usually related to knowledge. Here, we present a broader picture in which information is associated with epistemic structures, which form cognitive infological systems as basic recipients and creators of cognitive information. Infological systems are modeled by epistemic spaces, while operators in these spaces are mathematical models of information. Information that acts on epistemic structures is called cognitive information, while information that acts on knowledge structures is called epistemic information. The latter brings new and updates existing knowledge, being of primary importance to people. In this paper, both types of information are studied as operators in epistemic spaces based on the general theory of information. As a synthetic approach, which reveals the essence of information, organizing and encompassing all main directions in information theory, the general theory of information provides efficient means for such a study. Different types of information dynamics representation use tools from various mathematical disciplines, such as the theory of categories, functional analysis, mathematical logic and algebra. In this paper, we base our exploration of information and knowledge dynamics on functional analysis further developing the mathematical stratum of the general theory of information.
Information2014, 5(2), 319-356; doi:10.3390/info5020319 - published 26 May 2014 Show/Hide Abstract
Abstract: Simulating organizational processes characterized by interacting human activities, resources, business rules and constraints, is a challenging task, because of the inherent uncertainty, inaccuracy, variability and dynamicity. With regard to this problem, currently available business process simulation (BPS) methods and tools are unable to efficiently capture the process behavior along its lifecycle. In this paper, a novel approach of BPS is presented. To build and manage simulation models according to the proposed approach, a simulation system is designed, developed and tested on pilot scenarios, as well as on real-world processes. The proposed approach exploits interval-valued data to represent model parameters, in place of conventional single-valued or probability-valued parameters. Indeed, an interval-valued parameter is comprehensive; it is the easiest to understand and express and the simplest to process, among multi-valued representations. In order to compute the interval-valued output of the system, a genetic algorithm is used. The resulting process model allows forming mappings at different levels of detail and, therefore, at different model resolutions. The system has been developed as an extension of a publicly available simulation engine, based on the Business Process Model and Notation (BPMN) standard.
Information2014, 5(2), 305-318; doi:10.3390/info5020305 - published 15 May 2014 Show/Hide Abstract
Abstract: Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS) sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP) and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE) database. Compared with other widely used methods such as linear support vector machines (SVM), sparse representation-based classifier (SRC), nearest subspace classifier (NSC), K-nearest neighbor (KNN) and radial basis function neural networks (RBFNN), the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.