Volume 18 (2018): Edizione 3 (September 2018)

The analysis of child’s speech corpora shows that the process of acquisition of English and French displays identical development of children’s expressions when the speech-utterances are presented as Fibonacci-weighted classes of concepts. A model of concept complexity and information processing based on principles of optimality is proposed to explain this statistical result.

Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.

In this paper we study the Hausdorff approximation of the shifted Heaviside step function h_t0(t) by sigmoidal functions based on the Chen’s and Pham’s cumulative distribution functions and find an expression for the error of the best approximation. We give real examples with data provided by IBM entry software package and Apache HTTP Server using Chen’s software reliability model and Pham’s deterministic software reliability model. Some analyses are made.

In this paper, fault tolerant task scheduling algorithms are proposed for mapping task graphs to heterogeneous processing nodes. These scheduling heuristics that we propose are redundancy-based software to tolerate hardware faults. We consider only processor permanent failures with a fail-silent behavior. The proposed heuristics generate automatically a real-time fault distributed schedule of dependent and independent tasks into heterogonous multiprocessors architecture. The heuristics are based on active and passive redundancy.

Since canonical PSO method has many disadvantages which do not allow to effectively reach the global minima of various functions it needs to be improved. The article refers to a novel Multi-Epoch Particle Swarm Optimization (ME-PSO) technique which has been developed by authors. ME-PSO algorithm is based on reinitializing of the stagnant swarm with low exploration efficiency. This approach provides a high rate of global best changing. As a result ME-PSO has great possibility of finding good local (or even global) optimum and does not trap in bad local optimum. In order to prove the advantages of the ME-PSO technique numerical experiments have been carried out with ten uni- and multimodal benchmark functions. Analysis of the obtained results convincingly showed significant superiority of ME-PSO over PSO and IA-PSO algorithms. It has been set that canonical PSO is a special case of ME-PSO.

We propose a multi-layer cluster based energy aware routing protocol for Low Power and Lossy Networks, which divides the network area into equal length rings. The intra-ring clustering process divides a ring into equal sized clusters and inter-cluster routing applies the fuzzy logic to select the best route for data transfer. It increases the network lifetime and packet delivery ratio by 18-22% and 5-8%, respectively.

Single Sign-On (SSO) decreases the complexity and eases the burden of managing many accounts with a single authentication mechanism. Mission critical application such as banking demands highly trusted identity provider to authenticate its users. The existing SSO protocol such as OpenID Connect protocol provides secure SSO but it is applicable only in the consumer-to-social-network scenarios. Owing to stringent security requirements, the SSO for banking service necessitates a highly trusted identity provider and a secured private channel for user access. The banking system depends on a dedicated central banking authority which controls the monetary policy and it must assume the role of the identity provider. This paper proposes an extension of OpenID Connect protocol that establishes a central identity provider for bank users, which facilitates the users to access different accounts using single login information. The proposed Enhanced OpenID Connect (EOIDC) modifies the authorization code flow of OpenID Connect to build a secure channel from a single trusted identity provider that supports multiple banking services. Moreover, the EOIDC tightens the security mechanism with the help of SAT to avoid impersonation attack using replay and redirect. The formal security analysis and validation demonstrate the strength of the EOIDC against possible attacks such as impersonation, eavesdropping, and a brute force login. The experimental results reveal that the proposed EOIDC system is efficient in providing secured SSO protocol for banking services.

Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

Based on the simplified fish motion model, a robot fish which could detect the oil leakage point of pipeline was designed by the method of single-joint driving. The Hawkeye OV7725 was used to design the image acquisition module to obtain the current movement of the fish and the current pipeline situation and the collected data was processed for making the relevant decisions to achieve the direction of movement control with the STM32 microcontroller. On the basis of binarization image centroid method, the image recognition algorithm was studied. By using the coordinates of the white point in the two-dimensional array, a linear regression equation which can reflect the distribution trend of the white point in a frame image was designed and the motion direction of the current robot could be detected. Since the linear regression equation converge to the characteristics of discrete data points, the oil leakage point inside the white area of the image could be detected. Experiment results showed that the robot fish can effectively complete the oil spill point detection task.