Identity Governance Framework for Privileged Users

Information technology companies have grown in size and recognized the need to protect their valuable assets. As a result, each IT application has its authentication mechanism, and an employee needs a username and password. As the number of applications increased, as a result, it became increasingly complex to manage all identities like the number of usernames and passwords of an employee. All identities had to be retrieved by users. Both the identities and the access rights associated with those identities had to be protected by an administrator. Management couldn’t even capture such access rights because they couldn’t verify things like privacy and security. Identity management can help solve this problem. The concept behind identity management is to centralize identity management and manage access identity centrally rather than multiple applications with their authentication and authorization mechanisms. In this research work, we develop governance and an identity management framework for information and technology infrastructures with privileged access management, consisting of cybersecurity policies and strategies. The results show the efficiency of the framework compared to the existing information security components. The integrated identity and access management and privileged access management enable organizations to respond to incidents and facilitate compliance. It can automate use cases that manage privileged accounts in the real world.     

Two layered Genetic Programming for mixed-attribute data classification

The important problem of data classification spans numerous real life applications. The classification problem has been tackled by using Genetic Programming in many successful ways. Most approaches focus on classification of only one type of data. However, most of the real-world data contain a mixture of categorical and continuous attributes. In this paper, we present an approach to classify mixed attribute data using Two Layered Genetic Programming (L2GP). The presented approach does not transform data into any other type and combines the properties of arithmetic expressions (using numerical data) and logical expressions (using categorical data). The outer layer contains logical functions and some nodes. These nodes contain the inner layer and are either logical or arithmetic expressions. Logical expressions give their Boolean output to the outer tree. The arithmetic expressions give a real value as their output. Positive real value is considered true and a negative value is considered false. These outputs of inner layers are used to evaluate the outer layer which determines the classification decision. The proposed classification technique has been applied on various heterogeneous data classification problems and found successful.     

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.     

Single-row mapping and transformation of connected graphs

In a dimensional problem, the transformation of a graph into its linear network can be viewed as a transition involving demand and supply. A connected graph represents the demand flows between the components in the graph while the network supporting it is the resource or capacity links for supporting the demand volumes. The transformation involves a mapping from the graph to its network to satisfy certain performance metrics. In this work, we propose a model for transforming a connected graph to its linear network model in the form of a single-row routing network. The main objective is to provide an optimum routing network that minimizes the congestion. In this technique, the given graph is first partitioned into several disjoint cliques using the Hopfield neural network using our model called AdCliq. From the cliques, a set of intervals derived from the zones are obtained through the matching nodes in the single-row axis. The intervals are then mapped into a network of non-crossing nets using our previously developed tool called ESSR. The network is optimal in terms of minimum street congestion and number of doglegs, and this provides a reasonably good step towards the overall solution to the demand-supply problem.     

STRONG STABILIZATION OF MIMO SYSTEMS: A PROJECTION APPROACH

In this paper, the strong stabilization problem of multivariable linear time‐invariant systems is considered. The approach is based on state‐space stable projection. The formulation is reduced to find a full rank constant output feedback. The computations involved are simpler than other methods.     

Service based meta-model for the development of distributed embedded real-time systems

The development complexity of Distributed Embedded Real-Time Systems (DERTS) can be reduced by the use of Service-Oriented Computing (SOC). However, the existing modeling methods allow modeling of either DERTS or SOC concepts and there is a lack of meta-model for Service-Oriented development of DERTS. This paper proposes a service-based meta-model for DERTS, along with the constraints of the elements of the meta-model. A Smart Home case study was designed to validate the meta-model. This meta-model could not only be beneficial for Service-Oriented development of DERTS, but can also be used at the Platform Independent Model (PIM) level of MDD.     

Microwave-assisted pre-ionization experiments on GLAST-Ⅲ

GLAss Spherical Tokamak(GLAST-Ⅲ) is a spherical tokamak with an insulating vacuum vessel that has a unique single-passage capability for incident microwaves.In this work,electron cyclotron resonance heating(ECRH)-assisted plasma pre-ionization in GLAST-Ⅲ is explored for three radio-frequency(RF) polarizations(the O-,X-,and M-modes) at different toroidal-field(TF) strengths and filled gas pressures.The optimum hydrogen pressure is identified for efficient plasma pre-ionization.A comparison of the plasma pre-ionizations initiated by the O-,X-,and M-modes shows prominent differences in the breakdown time,location,and wave absorption.In the case of O-mode polarization,microwave absorption occurs for a relatively shorter duration,resulting in a bell-shaped electron-temperature(T_e) temporal profile.Microwave absorption is dominant in the case of the X-mode,leading to a broader T_e temporal profile.The M-mode discharge contains features of both the X-and O-modes.Efficient plasma pre-ionization is achieved in the X-mode polarization for the intermediate TF strengths(with a central toroidal magnetic field B_0=0.075 T).Traces of the electron-number density show a similar tendency,as revealed by T_e.These results suggest that the X-mode is the best candidate for efficient plasma pre-ionization at low filled gas pressures(~10~(-2) Pa) in small tokamaks.     

Numerical solutions of two dimensional Sobolev and generalized Benjamin–Bona–Mahony–Burgers equations via Haar wavelets

This work addresses, numerical method based on Haar wavelets and finite differences to solve two dimensional linear, nonlinear Sobolev and non-linear generalized Benjamin–Bona–Mahony–Burgers (NGBBMB) equations. The temporal part is discretized using finite differences while spatial part is approximated by two dimensional Haar wavelets. With this strategy, computing solution of two dimensional PDEs reduces to computing solution of linear system of algebraic equations. Collocation approach is then applied to determine the wavelet coefficients from linear system. This paper shows that two dimensional Haar wavelets are suitable and effective for two dimensional linear and non-linear PDEs. For validation of the proposed scheme different problems have been solved and error norms $L_{\infty },L_2$ are computed. Computation verifies that current scheme has good outcome.     

Characterisation of Malaysian durian (Durio zibethinus Murr.) cultivars: Relationship of physicochemical and flavour properties with sensory properties

The physicochemical (pH, soluble solids, titratable acidity, sugars and organic acids), flavour and sensory properties of five Malaysian durian cultivars (D2, D24, MDUR78, D101 and Chuk) were studied. There were significant differences (P < 0.05) among the five cultivars in terms of all physicochemical characteristics tested with the exception for D2 and MDUR 78, which had similar physicochemical characteristics. Twenty two esters, 14 sulphur compounds, 7 alcohols, 3 aldehydes and 1 ketone were detected in the durian pulp of the five different cultivars using solid-phase microextraction coupled to gas chromatography-time of flight mass spectrometry. Diethyl disulphide, ethyl-n-propyl disulphide, diethyl trisulphide and ethanethiol were the predominant sulphur-containing compounds in all the cultivars. The major esters present in durian were either ethyl propanoate, ethyl-2-methyl butanoate, or propyl-2-methylbutanoate and their levels varied within cultivars. Principal component analysis applied to the data differentiated all cultivars based on 29 volatile flavour compounds exhibiting significant differences (P < 0.05) between cultivars. Principal components 1 and 2 explained 89% of the total variance. A strong correlation was observed between sensory properties with flavour compound and physicochemical characteristics of the fruit.