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Banking and Financial Services Corporations need to update information systems with logic and data of various applications on everyday basis to remain consistent with change in economy and business activity. This helps to work with latest information included in information system available in current economic and business scenario. This enables information systems and empowers work force to complete tasks in rapidly changing flow of monetary resources. With several employees in Banking and Financial Services Corporations using cloud infrastructure and reporting incidents arising in using cloud infrastructure, it is of prime importance to fix incidents reported within specific timelines. IT change management process is followed in order to adhere to IT governance & compliance framework and reduce risk of failure while performing changes in cloud infrastructure. With incident management and change management processes are aligned to keep cloud infrastructure available and secure, they become integral part of IT operations everyday activity. To make ITIL processes efficient, further organization specific policies are developed. With global standards and organization level controls in place, there are failures in IT incident and change management processes and implementation. In this paper, we have identified the risk arising due to incident management and change management processes that lead to emergency changes being implemented on cloud infrastructure architecture and discussed the steps to mitigate risks to bring greater responsibility and accountability for cloud services providers.

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19’s economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19.

New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology.

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients.

There are a variety of well-designed specification-modelling-languages serving Internet services, however, none of them is capable of describing the special features of cloud services, from both technical and business points of view. The Unified Service Description Language (USDL) provides a new way to describe Internet services from business, operational, and technical perspectives. Nevertheless, there are various issues with USDL: it lacks a comprehensive specification model, particularly for cloud services, lacks a user-centric specification modeling paradigm, lacks a mechanism to measure cloud service attributes and to present the association relationship and re-usability of the attributes, and lacks semantic representation of cloud services. Based on the above issues, we propose a unified semantic Cloud Service Description Model (CSDM) in this paper. The proposed model will be extended from the basic structure of USDL, by defining cloud-service-specific attributes. Furthermore, an additional module, named transaction module, will be defined, which models the rating system of cloud services from several aspects, such as risk, trust, and reputation. The transaction module facilitates the capability of CSDM with regard to service ranking, and enhances its flexibility and extensibility by providing an extensible sub-module. In addition, we design an OWL-based annotation system to enrich the semantic expressivity of this model. Finally, a case study is provided to explain the application of this model in actual cloud services.

Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners’ behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.

Technology plays an important role in helping organizations control quality and costs, and take advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to confirm the factors to be considered for cloud computing adoption in Australian regional municipal governments. The research involved data from interviews with IT managers from selected regional municipal governments, and survey data from 480 IT staff across 47 regional municipal governments. The major factors to be considered for the adoption of cloud computing in regional municipal governments were identified as Internet connectivity, Internet speed, availability, reliability, data storage location, security, data sovereignty, cost, integration, data backup, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when regional municipal governments planning to adopt cloud computing.

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

Wireless communication is one of the key technologies that actualize the Internet of Things (IoT) concept into the real world. Understanding the security performance of wireless communications lays the foundation for the security management of IoT. Eavesdropper collusion represents a significant threat to wireless communication security, while physical-layer security serves as a promising approach to providing a strong form of security guarantee. This paper studies the important secrecy outage performance of wireless communications under eavesdropper collusion, where the physical layer security is adopted to counteract such attack. Based on the classical Probability Theory, we first conduct analysis on the secrecy outage of the simple noncolluding case in which eavesdroppers do not collude and operate independently. For the secrecy outage analysis of the more hazardous M-colluding scenario, where any $M$ eavesdroppers can combine their observations to decode the message, the techniques of Laplace transform, keyhole contour integral, and Cauchy Integral Theorem are jointly adopted to work around the highly cumbersome multifold convolution problem involved in such analysis, such that the related signal-to-interference ratio modeling for all colluding eavesdroppers can be conducted and thus the corresponding secrecy outage probability can be analytically determined. Finally, simulation and numerical results are provided to illustrate our theoretical achievements. An interesting observation suggests that the SOP increases first superlinearly and then sublinearly with $M$ .

Purpose– The purpose of this paper is to report on research that evaluates the perceived willingness of potential bidders to adopt public e-procurement for the supply of goods and services to the government of Nepal. The authors have identified anti-corruption attributes through an extensive literature review and developed a theoretical model representing the impact of four latent variables, monopoly of power, information asymmetry, trust and transparency and accountability on the dependent variable, the intent-to-adopt e-procurement (ITA).Design/methodology/approach– Data for this research were obtained by the use of a questionnaire survey of bidders who were officially registered with the Government of Nepal. As part of the fieldwork for this research, the first author collected the perceptions of 220 bidders regarding the potential of public e-procurement to reduce corruption in public procurement processes.Findings– The findings suggest that a high level of the ITA has a positive and significant relationship with the independent variables that might inform the developed and emerging countries to make a decision to adoption of e-procurement to combat corruption in public procurement.Research limitations/implications– This study has some limitations that should be taken into consideration. The evaluation of anti-corruption factors, as they affect the willingness of users to adopt e-procurement on the bidder’s perception research model is relatively new to e-procurement research. A limitation of the research was that it gathered and analyzed data from a single country with a limited number of respondents. More research is needed to identify the anti-corruption factors of e-procurement in reducing corruption, and also need strong empirical test to valid the factors that influence the adoption of e-procurement.Originality/value– This study aimed to contribute to the academic scholar, government agencies and public procurement practitioner in enhancing their understanding of the perceived anti-corruption factors of public e-procurement to reduce corruption.

The seven papers in this special section focus on the research and development of service-oriented collaborative computing technologies and their applications to the design of products, processes, systems and services in an industrial and social viewpoint.

This paper presents a duplication-less storage system over the engineering-oriented cloud computing platforms. Our deduplication storage system, which manages data and duplication over the cloud system, consists of two major components, a front-end deduplication application and a mass storage system as back-end. Hadoop distributed file system HDFS is a common distribution file system on the cloud, which is used with Hadoop database HBase. We use HDFS to build up a mass storage system and employ HBase to build up a fast indexing system. With a deduplication application, a scalable and parallel deduplicated cloud storage system can be effectively built up. We further use VMware to generate a simulated cloud environment. The simulation results demonstrate that our deduplication storage system is sufficiently accurate and efficient for distributed and cooperative data intensive engineering applications.

Technology plays an important role in helping organisations control quality and costs, and taking advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to investigate the factors to be considered for cloud computing adoption by Australian regional councils. The research employed in-depth interviews with IT managers from selected local government councils. The major factors to be considered for the adoption of cloud computing were identified as Internet connectivity, data storage location, cost, integration, data back-up, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when planning to adopt cloud computing.