An extensive Review of Web-Based Multi Granularity Service Composition

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.

Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

Determinants of Auditor Choice in Non-Financial Listed Firms on the Vietnamese Stock Market

Audit plays an important role in maintaining and issuing high-quality financial statements. This article investigates the factors that can affect auditor choice in developing countries. The authors utilize STATA to test Binary Logistic on a sample of Vietnamese listed firms data during the period between 2014 and 2017. These studies have examined the characteristics of the firm itself or the client's characteristics, prompting the process of selecting an auditor in the same regulatory environment. The results present that there is a positive relationship between firm size, firm growth, and auditor choice. While financial leverage has a negative relationship with the selection of audit firms.

A Secure Energy Aware Game Theory (SEGaT) Mechanism for Coordination in WSANs

A novel secure energy aware game theory (SEGaT) method has proposed to have better coordination in wireless sensor actor networks. An actor has a cluster of sensor nodes which is required to perform different action based on the need that emerge in the network individually or sometime with coordination from other actors. The method has different stages for the fulfilment of these actions. Based on energy aware actor selection (EAAS), selection of number of actors and their approach is the initial step followed by the selection of best team of sensors with each actor to carry out the action and lastly the selection of reliable node within that team to finally nail the action into place in the network for its smooth working and minimum compromise in the energy The simulations are done in MATLAB and result of the energy and the packet delivery ratio are compared with game theory (GaT) and real time energy constraint (RTEC) method. The proposed protocol performs better in terms of energy consumption, packet delivery ratio as compared to its competitive protocols.