Asadullah Shah

Cloud Computing Adoption Model for Universities to Increase ICT Proficiency

Universities around the world especially those in developing countries are faced with the problem of delivering the level of information and communications technology (ICT) needed to facilitate teaching, learning, research, and development activities ideal in a typical university, which is needed to meet educational needs in-line with advancement in technology and the growing dependence on IT. This is mainly due to the high cost involved in providing and maintaining the needed hardware and software. A technology such as cloud computing that delivers on demand provisioning of IT resources on a pay per use basis can be used to address this problem. Cloud computing promises better delivery of IT services as well as availability whenever and wherever needed at reduced costs with users paying only as much as they consume through the services of cloud service providers. The cloud technology reduces complexity while increasing speed and quality of IT services provided; however, despite these benefits the challenges that come with its adoption have left many sectors especially the higher education skeptical in committing to this technology. This article identifies the reasons for the slow rate of adoption of cloud computing at university level, discusses the challenges faced and proposes a cloud computing adoption model that contains strategic guidelines to overcome the major challenges identified and a roadmap for the successful adoption of cloud computing by universities. The model was tested in one of the universities and found to be both useful and appropriate for adopting cloud computing at university level.

On Dynamic Communication Performance of a Hierarchical 3D-Mesh Network

A Hierarchical 3D-Mesh (H3DM) Network is a 2D-mesh network of multiple basic modules (BMs), in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. In this paper, we evaluate the dynamic communication performance of a Hierarchical 3D-Mesh (H3DM) network using a deadlock-free routing algorithm with minimum number of virtual channels under the uniform and non-uniform traffic patterns; and compare it with other networks to show the superiority of the H3DM network over other networks. We have also evaluated the dynamic communication performance of the mesh and torus networks. It is shown that H3DM network yields low average transfer time than that of mesh and torus networks. The trade-off between throughput and latency of these networks shown that H3DM network provide better dynamic communication performance than that of mesh and torus networks before saturation.

Widget based automated rigging of bipedal character with custom manipulators

In this research paper we present an automated rigging system for a humanoid (biped) character with complete customization and control options according to the need of the character rigger and the animator. The rigging requirements check list has been proposed for each body part by analyzing various live natural human motions and then summarizing them. The system initially provides widgets that represent human skeletal joints, which can be easily manipulated and modified according to the character type and body height. The system takes the widgets positions from the 3D space and creates the joint based skeletal structure automatically using procedural programming and then followed by creating a complete bipedal rig with custom manipulators to satisfy the animators need in a production environment.

Fault tolerant cloud auditing

The aim of cloud computing is to deliver on-demand services to its users with guarantee of scalability, availability and reliability. These services have a variety that may include simple and complex software, infrastructure and architecture. It may vary according to the Cloud Service Provider (CSP) and the service license agreement (SLA) with Cloud Client. The research contribution in this paper has two facets. First, an investigative review on cloud computing services, models, security and privacy concerns, and auditing strategies is conducted. Secondly, we suggest the fault tolerant approach in conjunction with the tiered strategy for cloud auditing.

A Security-Based Survey and Classification of Cloud Architectures, State of Art and Future Directions

This research paper aims to explore the underlying cloud computing architectures with respect to security and auditing services. As most of the standards have not been defined yet for cloud computing, academic and industrial researchers are participating with their ideas and proofs. We have surveyed and classified the available cloud computing architectures into three categories which are 1) Architecture of Cloud Computing Security and Auditing, 2) Data Storage Security, Privacy and Auditing of Data Leakage, and 3) Security and Auditing of Malicious Acts. This taxonomy and analysis of existing cloud architectures will be helpful to address the primary issues focusing the security and auditing perspectives in future research.

Sharing of attacks information across clouds for improving security: A conceptual framework

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. It is evolving as a key computing platform for sharing resources that include infrastructures, software, applications, and business processes. The cloud environment is required to have efficient security techniques as it contains precious assets such as data, users, technologies, resources and business transactions. Most of the proposed architectures for cloud computing implementations are for individual clouds which do not allow the sharing of attack information with other clouds and hence the other clouds are not able to safe themselves from the same attack. The aim of this research is to propose a conceptual framework for connected or federated clouds that can share information regarding attacks and attackers for securing themselves.

ReSA: Architecture for resources sharing between clouds

Cloud computing has emerged as paradigm for hosting and delivering services over the Internet. It is evolved as a key computing platform for delivering on-demand resources that include infrastructures, software, applications, and business processes. Mostly, clouds are deployed in a way that they are often isolated from each other. These implementations cause lacking of resources collaboration between different clouds. For example, cloud consumer requests some resource and that is not available at that point in time. Client satisfaction is important for business as denying the client may be expensive in many ways. To fulfill the client request, the cloud may ask the requested resource from some other cloud. In this research paper we aim to propose a trust worthy architecture named ReSA (Resource Sharing Architecture) for sharing on-demand resources between different clouds that may be managed under same or different rules, policies and management.

Systematic review of computational modeling of mood and emotion

In the recent years, more studies that aim to make computers understand, experience and respond to affects accordingly through computational models have been widely researched. Although many studies have defined and distinguished the words affect, mood and emotion, such terms are still used interchangeably. Thus, in this study, a systematic literature review was implemented to summarize and evaluate the current states of the arts on computational modeling of mood. From three online databases including IEEE Xplore, ScienceDirect and Springer Link, 825 scientific articles were extracted. Furthermore, through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) Statement, 9 articles were selected for the review. These resulting articles were reviewed based on several categories including the aim of the study, the study population, the measurement of mood, the basic emotion dimension, and proposed computational model, as well as evaluation. As a result, the systematic literature review has provided a good starting point in the study of the computational modeling of mood and emotion.

Analysis of SNs popularity from different perspectives among users

Social networking is unquestionably one of the most vital and influential aspect of todays society. A great number of Social Networks (SNs) are available now a days and being used by the people all around the globe. This paper provides an analysis of four most popular interactive SNs that are, Facebook, LinkedIn, Twitter and MySpace according to different perspectives. The data has been collected from secondary sources that has been used in this paper. The data has been analyzed and compared by four different perspectives based on user traffic, age, gender, and education of the users. The analysis of data indicate some useful and new insights such as the Facebook is the most popular network having the highest number of users. LinkedIn network, in the age groups between 34 to 45 years having the highest number of users. Interestingly LinkedIn, for gender base analysis, for the age groups of 34-45, numbered less than male users. This indicate that male professional out number than female users for the same age groups (34-45). Overall findings show that female users are 66% and rest 34% are male users for all four social networks that were analyzed in this research work.

Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs)

The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.