In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A RANDOM KEY and the related mode list (ML) REPRESENTATION scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has a better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.

RANDOM KEY Distribution (RKD) schemes have been widely accepted to enable low-cost secure communications in Wireless Sensor Networks (WSNs). However, efficiency of secure link establishment comes with the risk of compromised communications between benign nodes by adversaries who physically capture sensor nodes. The challenge is to enhance resilience of WSN against node capture, while maintaining the flexibility and low cost features of RKD schemes. We address this problem by proposing an effective technique, namely KDel, which don’ t require any special-purpose hardware or expensive computations. We demonstrate that our approach significantly increases the resilience of RKD schemes against node capture at the cost of a little communications, while maintaining network connectivity at the same level. Moreover, our scheme is generally applicable as it can improve the resilience of any RKD scheme.

Sybil attacks pose a serious threat for Wireless Sensor Networks (WSN) security. They can create problems in routing, voting schemes, decision making, distributed storage and sensor re-programming. In a Sybil attack, the attacker masquerades as multiple sensor identities that are actually controlled by one or a few existing attacker nodes. Sybil identities are fabricated out of stolen KEYs, obtained by captured benign nodes. Existing Sybil defensive mechanisms suffer from the restricted sensor network size, or cause excessive resource consumption for the sensor network. In this work we propose a Sybil node detection mechanism, based on RANDOM KEY Distribution (RKD) schemes that can cope with large network sizes and minimize the waste of resources. We explain the techniques each node can use in a network running q-composite RKD to detect Sybil identities and restrict their number. Our method requires no trust to other sensors, which is important to defend against the attack.

The purpose of this study was to investigate changes in mental REPRESENTATION in golf putting task due to RANDOM and blocked practice. Thirty participants were divided into RANDOM, blocked and control groups. First, the initial mental REPRESENTATION level of participants was measured and they performed 18 trials as pre-test. Then, they practiced the intended task for six consecutive days. A week after the last practice session their final mental REPRESENTATION level was measured and performed 18 trials as retention test. Results showed that RANDOM group had more errors than blocked group during acquisition, but performed more accurate than this group during retention. In addition, it was showed that RANDOM group have a more structured mental REPRESENTATION than blocked group and this REPRESENTATION is more like the skilled participants. This results showed that the cause of better learning by RANDOM group than blocked group probably is due to production of a more structured mental REPRESENTATION of the task.

In this letter, the RANDOM matrix theory REPRESENTATION of a bond-percolation model on square lattice is presented. The behavior of RANDOM matrix model can be determined only by its two largest eigenvalues. The second largest eigenvalue sits exactly on the edge of semicircle part of eigenvalue’s distribution and its position is a function of p, on the other hand the first largest eigenvalue is disjointed from other eigenvalues and its distribution is Gaussian. Also the first largest eigenvalue is responsible for scaling properties near criticality. Numerical simulations show power-law divergences emerged from coalescence of two largest eigenvalues near critical point at the thermodynamic limit. In this letter a scaling formalism is presented which describes complete scaling behavior of largest eigenvalue’s fluctuations with a set of scaling exponents in finite-size systems.

In this paper, we design a lightweight and modified RANDOM KEY generation for PRESENT block cipher which is applicable in the encryption of the digital signals. In the block ciphers, the master KEY is used directly in the encryption process for the data (plaintext). But in this work, a master KEY (initial KEY) is used to derive the new RANDOM master KEYs (RANDOM session KEYs) and use these KEYs for the encryption process. The use of RANDOM KEYs will overcome the brute force attack that can be applied to the PRESENT cipher. The RANDOM session KEYs generated will produce different ciphertexts for the same plaintext for every session. In this approach, we take advantage of the block cipher to produce RANDOM KEYs. The PRESENT cipher is shared in both RANDOM KEY generation and encryption process. Therefore, the proposed structure has both RANDOM KEY generation and data encryption in a unified circuit. This property reduces hardware resources. The implementation results, in 180 nm CMOS technologies, show the proposed structure is comparable in terms of area and delay with other works.

This paper uses the unique biometric features of fingerprints to generate RANDOM cryptographic KEYs. The main aspects of the security of the generated KEY include the privacy of the fingerprint and the RANDOMness and complexity of the KEY generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be RANDOM from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated KEYs show the acceptable RANDOM nature of the KEYs. Therefore, the proposed structure for generating a RANDOM KEY can be used in encrypting digital signals with large volumes of data such as image and sound.