Informatica

This paper proposes a novel approach to light plane labeling in depth-image sensors relying on “uncoded” structured light. The proposed approach adopts probabilistic graphical models (PGMs) to solve the correspondence problem between the projected and the detected light patterns. The procedure for solving the correspondence problem is designed to take the spatial relations between the parts of the projected pattern and prior knowledge about the structure of the pattern into account, but it also exploits temporal information to achieve reliable light-plane labeling. The procedure is assessed on a database of light patterns detected with a specially developed imaging sensor that, unlike most existing solutions on the market, was shown to work reliably in outdoor environments as well as in the presence of other identical (active) sensors directed at the same scene. The results of our experiments show that the proposed approach is able to reliably solve the correspondence problem and assign light-plane labels to the detected pattern with a high accuracy, even when large spatial discontinuities are present in the observed scene.

In the hiring process at companies, decision makers have underused the methods of the multi-criteria decision-making processes of selection of personnel. Therefore, this paper aims to establish a framework for the selection of candidates during the process of the recruitment and selection of personnel based on the SWARA and ARAS methods under uncertainties. The usability and efficiency of the proposed framework is considered in the conducted case study of the selection of candidate for the position of the sales manager.

Denial-of-service (DoS) attacks against server resources exhaustion are a major security threat to the Internet. A number of defense mechanisms have been proposed against such attacks. Recently, Aura et al. proposed a solution to resist DoS attacks against an authentication protocol. However, their puzzle solution cannot guarantee that all of their clients have fair computation time to solve a puzzle. The solution may even render some clients unable to obtain the puzzle solution within the lifetime, resulting in a lack of service from the server. In this paper, a simple solution as well as an applied authentication protocol was proposed.

Model-driven IS engineering methods invoke the IS application domain modelling methods to acquire essential characteristics of organizational systems (enterprises). Business modelling for value creation is a relatively separate area, meanwhile it correlates with the IS application domain modelling methodologies and gives new insights for enhancement of enterprise modelling, business process modelling and BP management modelling approaches. The IS application domain and the business domain modelling are not isolated and could be investigated using the same paradigm of modelling. Yet there is some uncertainty in model-driven approaches towards the understanding of the enterprise management activities. A problematic consistency of modelling approaches indicate a need for a systemic analysis of IS application domain modelling concepts. The internal modelling paradigm is used for analysis of an enterprise management activity as a self-managed system, and hereby the lack of the conceptual basis for domain modelling in IS engineering is determined. This approach is aimed to reveal hidden information transactions of the business management activities. The understanding of the IS application domain as a self-managed system allowed to redefine such concepts as management transaction, management function and enterprise process. The metastructure of management transaction is defined and illustrated for business management layer and IS development layer.

Interval-valued intuitionistic fuzzy numbers (IVIFNs) characterized by a membership function and a non-membership function with values that are intervals, have strong ability to handle imprecise and ambiguous information in real-world applications. This paper proposes an integrated maximizing consistency and multi-choice goal programming (MCGP) approach to handle hybrid multi-criteria group decision making problems based on IVIFNs. Firstly, the hybrid decision information (including crisp numbers, intervals, intuitionistic fuzzy numbers and linguistic variables) are normalized into the IVIFNs. Then, an ordinal consistency index and a cardinal consistency index are proposed to measure the consistency between the individual opinion and the group opinion, respectively. And an optimal model based on maximizing consistency is constructed to derive the weights of experts. Afterwards, the comprehensive ratings and the ranking values of alternatives are obtained by the hybrid weighted aggregation operator and the proposed ratio function of IVIFNs, respectively. Furthermore, a MCGP model based on the ranking values is constructed to identify the optimal alternatives and their optimum quantities. At length, an illustrative case is provided to verify the proposed approach.

Fair input/output (or I/O) automata are a state-machine model for specifying and verifying reactive and concurrent systems. For the verification purposes, one is usually interested only in the sequences of interactions fair I/O automata offer to their environment. These sequences are called fair traces. The usual approach to the verification consists in proving fair trace inclusion between fair I/O automata. This paper presents a simple approach to the specification of fair traces and shows how to establish a fair trace inclusion relation for a pair of fair I/O automata by using the temporal logic of actions.

Certificateless public-key systems (CL-PKS) were introduced to simultaneously solve two critical problems in public-key systems. One is the key escrow problem in ID-based public-key systems and the other is to eliminate the presence of certificates in conventional public-key systems. In the last decade, several certificateless signature (CLS) schemes have been proposed in the random oracle model. These CLS schemes possess existential unforgeability against adaptive chosen-message attacks, and only few of them possess strong unforgeability. A CLS scheme with strong unforgeability plays an important role in the construction of certificateless cryptographic schemes. Unfortunately, all the existing CLS schemes in the standard model (without random oracles) have been shown insecure to provide existential unforgeability under a generally adopted security model. In the article, we propose a strongly secure CLS scheme in the standard model under the generally adopted security model. Our scheme possesses not only existential unforgeability but also strong unforgeability, and turns out to be the first strongly secure CLS scheme in the standard model. Under the collision resistant hash (CRH) and computational Diffie–Hellman (CDH) assumptions, we prove that our CLS scheme possesses strong unforgeability against both Type I (outsiders) and Type II (key generation center) adversaries.

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying experiments on test instances. We argue that a good minimum performance benchmark is due to pure random search; i.e. algorithms should do better. We introduce the concept of the cumulative distribution function of the record value as a measure with the benchmark of pure random search and the idea of algorithms being dominated by others. The concepts are illustrated using frequently used algorithms.

Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

The choice of natural image prior decides the quality of restored image. Recently successful algorithms exploit heavy-tailed gradient distribution as image prior to restore latent image with piecewise smooth regions. However, these prior assumed also remove the mid-frequency component such as textural details regions while they preserve sharp edges. That because gradient profile in fractal-like texture do not have sparse characteristic.