Informatica

This paper studies the generic construction of certificate-based signature (CBS) from certificateless signature (CLS). This paper proposes a new generic conversion from CLS to CBS which is more intuitive, simpler, and provably secure without random oracles than the current one. To develop the security proof, we put forth one novel CLS security model which features a previously neglected but nontrivial attack and hence captures the CLS security notion more comprehensively. We show that many existing CLS schemes can be proved secure in the current model by slightly modifying its original security proof. Following this conversion, many provably secure CBS schemes can be constructed from the corresponding existing CLS schemes.

Linguistic hesitant fuzzy sets (LHFSs) permit the decision maker to apply several linguistic terms with each having several membership degrees to denote his/her preference of one thing. This type of fuzzy sets can well address the qualitative and quantitative cognitions of the decision maker as well as reflect his/her hesitancy, uncertainty and inconsistency. This paper introduces a distance measure between any two LHFSs and then defines a correlation coefficient of LHFSs. Considering the application of LHFSs, the weighted distance measure and the weighted correlation coefficient of LHFSs are defined. To address the interactions between elements in a set, the Shapley weighted distance measure and the Shapley weighted correlation coefficient are presented. It is worth noting that when the elements are independent, they degenerate to the associated weighted distance measure and the weighted correlation coefficient, respectively. After that, their application to pattern recognition is studied. Furthermore, an approach to multi-attribute decision making under linguistic hesitant fuzzy environment is developed. Meanwhile, numerical examples are offered to show the concrete application of the developed procedure.

Many papers exist on ordinary fuzzy control charts in literature in order to consider the vagueness and uncertainty in observation data. These are on both variable and attribute control charts. Several extensions of fuzzy sets have appeared in literature since ordinary fuzzy sets emerged. Type-2 fuzzy sets are one of these extensions. Type-2 fuzzy sets take into account the imprecision of membership functions in three dimensions. The aim of this paper is to develop interval type-2 fuzzy control charts for number of nonconformities, briefly c-control charts. In this paper, the theoretical structure of interval type-2 fuzzy c-control charts is proposed for the first time and the application is implemented in a food company.

Machine type communication (MTC) systems are a new paradigm in communication systems where machines talk to each other rather than humans. It is expected that more than twenty billion smart devices are deployed around the globe by 2020. The machines talk to each other and communicate with cloud based MTC servers to monitor and control everything around us. Such ubiquitous sensing and actuating require a communication infrastructure. Cellular networks due to their wide coverage are the best candidate for the communication infrastructure. The 3GPP LTE system is the future cellular network. Access class barring (ACB) is introduced by the standard as a solution to alleviate the congestion at the access layer. It works as a persistent probability for network access at the data link layer. In this paper, we consider an MTC system with several devices using LTE system as communication network. Based on the suggestions of the 3GPP standard, we consider uniform activation of devices within a long interval. This activation pattern results in Poisson arrival traffic in each random access channel. Using this arrival traffic pattern, we obtain the ACB factor which maximizes the throughput in the access link. This factor depends on the traffic parameters. Then, we propose a scheme to estimate the traffic parameters. At the end, we propose an algorithm which takes into account practical considerations. We validate our analytical models through extensive simulations.

Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. Most algorithms assume feature independence when computing similarity between data objects, or make use of computationally demanding techniques such as PCA for numerical data. Hierarchical clustering algorithms are often based on similarity measures computed on a common feature space, which is not effective when clustering high-dimensional data. Subspace clustering algorithms discover feature subspaces for clusters, but are mostly partition-based; i.e. they do not produce a hierarchical structure of clusters. In this paper, we propose a hierarchical algorithm for clustering high-dimensional categorical data, based on a recently proposed information-theoretical concept named holo-entropy. The algorithm proposes new ways of exploring entropy, holo-entropy and attribute weighting in order to determine the feature subspace of a cluster and to merge clusters even though their feature subspaces differ. The algorithm is tested on UCI datasets, and compared with several state-of-the-art algorithms. Experimental results show that the proposed algorithm yields higher efficiency and accuracy than the competing algorithms and allows higher reproducibility.

In this paper, a new class of uncertain linguistic variables called 2-tuple linguistic hesitant fuzzy sets (2-TLHFSs) is defined, which can express complex multi-attribute decision-making problems as well as reflect decision makers’ hesitancy, uncertainty and inconsistency. Besides, it can avoid information and precision losing in aggregation process. Firstly, several new closed operational laws based on Einstein t-norm and t-conorm are defined over 2-TLHFSs, which can overcome granularity and logical problems of existing operational laws. Based on the new operational laws, 2-tuple linguistic hesitant fuzzy Einstein weighted averaging (2-TLHFEWA) operator and 2-tuple linguistic hesitant fuzzy Einstein weighted geometric (2-TLHFEWG) operator are proposed, and some of their properties are investigated. Then, a new model method based on similarity to ideal solution is proposed to determine weights of attribute, which takes both subjective and objective factors into consideration. Finally, a linguistic hesitant fuzzy multi-attribute decision making procedure is developed by means of 2-TLHFEWA and 2-TLHFEWG operators. An example is given to illustrate the practicality and efficiency of the proposed approach.

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

The popularity of sharing data through cloud services has increased these days. As a result, the security of data sharing has become an important issue. The security mechanism has to ensure that the shared data would not be intercepted or altered by illegal members during transmission. A data sharing scheme for cloud services is proposed in this paper to achieve the following four security requirements: 1) forward secrecy and backward secrecy, 2) source authentication, 3) data integrity, and 4) confidentiality. In addition, message recovery is applied to improve the efficiency of encryption and signature computation. The computation cost is reduced by computing a common key for all data. Thus, the data owner only needs to encrypt the shared data once before sending it in this proposed scheme.

This paper proposes the concepts of a neutrosophic number and a trapezoidal neutrosophic number (TNN), the basic operational relations of TNNs, and the score function of TNN. Then, we develop a trapezoidal neutrosophic weighted arithmetic averaging (TNWAA) operator and a trapezoidal neutrosophic weighted geometric averaging (TNWGA) operator to aggregate TNN information and investigate their properties. Furthermore, a multiple attribute decision making method based on the TNWAA and TNWGA operators and the score function of TNN is established under a TNN environment. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.

An adaptive multi-rate wideband (AMR-WB) speech codec with a sampling rate of 16 kHz is known as one of the speech codecs employed in handheld devices that support 4G mobile communication systems. When applied to smartphones, it provides a superior speech quality relative to conventional speech codecs. Nonetheless, a major disadvantage is that an algebraic codebook search occupies a significant computational load in an AMR-WB encoder. In other words, the high computational complexity accounts for the high power consumption on a smartphone battery. This paper presents an improved version of depth-first tree search (DF) algorithm as a means to considerably reduce the complexity of an algebraic codebook search in an AMR-WB speech codec. This proposed search algorithm firstly involves the choice of a specified number of candidate pulses according to a pulse contribution ranking. Subsequently, a DF search is performed on the candidate pulses for a set of best pulses. Consequently, the target of the search and computational complexity reduction can be reached as expected. With a well maintained speech quality, this proposal demonstrates a search performance superiority over a DF and a global pulse replacement approach. Furthermore, with DF as a benchmark, a computational load reduction above 73% is reached in all coding modes.