Information2015, 6(3), 314-338; doi:10.3390/info6030314 - published 2 July 2015 Show/Hide Abstract
Abstract: In this paper I give a new information-theoretic analysis of the formalisms and interpretations of quantum mechanics (QM) in general, and of two mainstream interpretations of quantum mechanics in particular: The Copenhagen interpretation and David Bohm’s interpretation of quantum mechanics. Adopting Juan G. Roederer’s reading of the notion of pragmatic information, I argue that pragmatic information is not applicable to the Copenhagen interpretation since the interpretation is primarily concerned with epistemology rather than ontology. However it perfectly fits Bohm’s ontological interpretation of quantum mechanics in the realms of biotic and artificial systems. Viewing Bohm’s interpretation of QM in the context of pragmatic information imposes serious limitations to the qualitative aspect of such an interpretation, making his extension of the notion active information to every level of reality illegitimate. Such limitations lead to the idea that, contrary to Bohm’s claim, mind is not a more subtle aspect of reality via the quantum potential as active information, but the quantum potential as it affects particles in the double-slit experiment represents the non-algorithmic aspect of the mind as a genuine information processing system. This will provide an information-based ground, firstly, for refreshing our views on quantum interpretations and secondly, for a novel qualitative theory of the relationship of mind and matter in which mind-like properties are exclusive attributes of living systems. To this end, I will also take an information-theoretic approach to the notion of intentionality as interpreted by John Searle.
Information2015, 6(3), 300-313; doi:10.3390/info6030300 - published 24 June 2015 Show/Hide Abstract
Abstract: In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference system (ANFIS) model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO) algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.
Information2015, 6(3), 287-299; doi:10.3390/info6030287 - published 24 June 2015 Show/Hide Abstract
Abstract: Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values. Firstly, we propose a classification method based on the incomplete sparse representation. This representation is boiled down to an l1 minimization problem and an alternating direction method of multipliers is employed to solve it. Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental results demonstrate that the proposed method has the superiority in classification accuracy, completion of the missing entries and recovery of noise.
Information2015, 6(2), 275-286; doi:10.3390/info6020275 - published 11 June 2015 Show/Hide Abstract
Abstract: Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom development and information retrieval from them. To solve this problem, we have been developing an office document query (ODQ) language which provides a uniform method to retrieve content from documents with different formats and versions. ODQ builds common document model ontology to conceal the format details of documents and provides a uniform operation interface to handle office documents with different formats. The results show that ODQ has advantages in format independence, and can facilitate users in developing documents processing systems with good interoperability.
Information2015, 6(2), 258-274; doi:10.3390/info6020258 - published 11 June 2015 Show/Hide Abstract
Abstract: One of the major applications of Radio Frequency Identification (RFID) technology is in supply chain management as it promises to provide real-time visibility based on the function of track and trace. However, such an RFID-based track and trace system raises new security and privacy challenges due to the restricted resource of tags. In this paper, we refine three privacy related models (i.e., the privacy, path unlinkability, and tag unlinkability) of RFID-based track and trace systems, and clarify the relations among these privacy models. Specifically, we have proven that privacy is equivalent to path unlinkability and tag unlinkability implies privacy. Our results simplify the privacy concept and protocol design for RFID-based track and trace systems. Furthermore, we propose an efficient track and trace scheme, Tracker+, which allows for authentic and private identification of RFID-tagged objects in supply chains. In the Tracker+, no computational ability is required for tags, but only a few bytes of storage (such as EPC Class 1 Gen 2 tags) are needed to store the tag state. Indeed, Tracker+ reduces the memory requirements for each tag by one group element compared to the Tracker presented in other literature. Moreover, Tracker+ provides privacy against supply chain inside attacks.
Information2015, 6(2), 246-257; doi:10.3390/info6020246 - published 8 June 2015 Show/Hide Abstract
Abstract: Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a need for understanding the relations between different types of delay and how they each contribute to the total delay of the entire intersection. In order to address these issues, this paper first examines models that focus on through and left-turn traffic delays, taking into account the presence of heavy mixed traffic flows that are prevalent in developing countries, then establishes a model for calculating right-turn traffic delay and, last, proposes an approach to analyzing how much each of the three types of traffic delay contributes to the total delay of the intersection, based on the application of shift-share analysis (SSA), which has been applied extensively in the field of economics.