In the field of Artificial Intelligence (AI), the data used to train models may contain private information that could potentially be exposed in the model’s output. Machine Unlearning (MU) has emerged as a promising solution for removing private or obsolete data from trained models, along with their influence, thereby enforcing the “right to be forgotten” under the General Data Protection Regulation (GDPR). However, achieving a balance between privacy guarantee and model performance remains a fundamental challenge. This paper contributes to the field of AI by presenting an empirical evaluation of key families, i.e., data deletion, data perturbation, and model update of MU for privacy preservation, focusing on their impact on both classification accuracy and privacy in deep learning (DL) models. The study assesses changes in the classification performance of the convolutional neural network (CNN) architecture and the long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural network (RNN) architectures when used with data deletion, data perturbation, and model update families of MU. This study also assesses these architectures’ susceptibility to membership inference attacks (MIA) before and after unlearning on PPG-DaLiA and MHEALTH (Mobile HEALTH) datasets, providing a quantitative measure of privacy leakage. Experimental results show that model update techniques offer more scalable alternatives to data deletion and perturbation, though they introduce varying levels of privacy leakage risk. In doing so, this research highlights the strengths and limitations of current targeted unlearning methods and underscores the need for more efficient and flexible approaches to privacy protection in DL models.

ABSTRACT The self‐organisation map is an unsupervised learning technique that discovers patterns and relationships in data without requiring labelled training data. Inspired by the self‐organisation map, Self‐Organised Granular encoding has been shown to be effective for generating reliable discrete data clustering results as it is a data encoding technique that uses fuzzy sets and granularity to handle uncertain and imprecise information within discrete data. However, it is mainly useful for unsupervised learning, and its feasibility for supervised learning has not yet been studied. Also, discrete data classification is still under‐researched. This paper proposes a new discrete data classification method called Transposed Fuzzy Class Granular classification. This method aims to transform discrete data into fuzzy partitions by considering all available classes and generating representations of the trained class's Transposed Fuzzy Class Granular distribution by measuring the total divergence from the average of each fuzzy class's membership degree distribution. The paper introduces a novel approach to discrete data classification by adapting class granules for classification and improving performance by tackling uncertainty, ambiguity, and the unique characteristics of discrete datasets. The study examined seven discrete datasets and compared their performance with eight commonly used classifiers as the baseline. These datasets were naturally discrete or created by discrete partitions of real datasets. The experimental results demonstrate that the proposed classifier outperforms the baseline classifiers in discrete data classification.

Classifying graph-structured data presents significant challenges due to the diverse features of nodes and edges and their complex relationships. While Graph Neural Networks (GNNs) are widely used for graph prediction tasks, their performance is often hindered by these intricate dependencies. Leveraging causality holds potential in overcoming these challenges by identifying causal links among features, thus enhancing GNN classification performance. However, depending solely on adjacency matrices or attention mechanisms, as commonly studied in causal prediction research, is insufficient for capturing the complex interactions among features. To address these challenges, we present HebCGNN , a Hebbian-enabled Causal GNN classification model that incorporates dynamic impact valuing . Our method creates a robust framework that prioritizes causal elements in prediction tasks. Extensive experiments on seven publicly available datasets across diverse domains demonstrate that HebCGNN outperforms state-of-the-art models.

Abstract The expansion of Graph Neural Networks (GNNs) has highlighted the importance of evaluating their performance in real-world scenarios. However, existing evaluation frameworks often overlook the integration of causality, a critical component that is essential for more robust evaluation of GNNs. To address this gap, we present a benchmark study that systematically compares standard and causal GNN models with a focus on classification tasks. Our analysis encompasses a careful selection of nine GNN models across seven diverse datasets that span three distinct domains. The results reveal the following: I) Causality-enhanced GNNs consistently outperform their traditional counterparts in graph classification tasks; II) Models integrating causal features exhibit greater generalizability across varied datasets; and III) Incorporation of causal elements significantly improves the predictive accuracy of GNNs. These findings highlight the importance of embedding causality in the evaluation and development of GNNs for improved performance and application.

Urban air quality prediction models can predict pollutant values based on its time series. Existing research shows that the correlation between influencing factors is dynamic. In this paper, we propose an Urban Air Quality Prediction Model based on Dynamic Correlation of Influencing Factors (DynamicAir) to address this problem. In the dynamic correlation module, the dynamic correlation of influencing factors is captured by dynamic graph generation and dynamic graph convolution; in the multi-time-step prediction module, the time correlation of each step and the dynamic correlation of influencing factors are mapped by multi-layer non-linear mapping to obtain the future pollutant concentration values at multi-steps. Experimental results on two real datasets(Beijing Capital International Airport and Beijing Olympic Sports Centre) show that the proposed DynamicAir reduces the RMSE by 1.15% and 4.04% respectively compared to the state-of-the-art baseline model (with a statistical interval of three hours).

Personality analysis can help individuals gain self-awareness, improve decision-making skills, and enhance relationships, while also providing valuable insights in fields like psychology, human resources, and marketing. Computational models, including traditional machine learning and deep learning models, have been beneficial in analyzing the impact of personality in sociological studies, especially deep learning models with interpretability. However, different computational models may produce different explanation results, which poses a challenge for social studies researchers in selecting appropriate explanations, as each model provides distinct prediction accuarcy values and explanation. To this end, this work introduces a computational personality analysis framework that incorporates Local Interpretable Model-Agnostic Explanations (LIME) to investigate analysis methods. The framework covers various computational personality models, encompasses pre-processing techniques, feature embedding, and focuses specifically on Myers-Briggs Type Indicator (MBTI) personality analysis, enabling valuable insights from predictions generated by diverse computational models. Cosine similarity is employed to evaluate the variations in explanation results produced by different computational models in relation to personality analysis. Our experiment reveals the key finding: although they have comparable predictive accuracy, there are not small explanation differences among the computational models.

Abstract The introduction of blockchain technology has brought about significant transformation in the realm of digital transactions, providing a secure and transparent platform for peer-to-peer interactions that cannot be tampered with. The decentralised and distributed nature of blockchains guarantees the integrity and authenticity of the data, eliminating the need for intermediaries. The applications of this technology are not limited to the financial sector, but extend to various areas, such as supply chain management, identity verification, and governance. At the core of these blockchains is the consensus mechanism, which plays a crucial role in ensuring the reliability and integrity of a system. Consensus mechanisms are essential for achieving an agreement amongst network participants regarding the validity of transactions and the order in which they are recorded on the blockchain. By incorporating consensus mechanisms, blockchains ensure that all honest nodes in the network reach a consensus on whether to accept or reject a block, based on predefined rules and criteria. The aim of this study is to introduce a novel consensus mechanism named Erdos, which seeks to address the shortcomings of existing consensus algorithms, such as the Proof of Work and Proof of Stake. Erdos emphasises security, decentralisation, and fairness. One notable feature of this mechanism is its equitable node-selection algorithm, which ensures equal opportunities for all nodes to engage in block creation and validation. In addition, Erdos implements a deterministic block finalisation process that guarantees the integrity and authenticity of the blockchain. The main contribution of this research lies in its innovative approach to deterministic block finalisation, which effectively mitigates the various security risks associated with blockchain systems.

Federated Learning (FL) is currently one of the most popular technologies in the field of Artificial Intelligence (AI) due to its collaborative learning and ability to preserve client privacy. However, it faces challenges such as non-identically and non-independently distributed (non-IID) data with imbalanced labels among local clients. To address these limitations, the research community has explored various approaches such as using local model parameters, federated generative adversarial learning, and federated representation learning. In our study, we propose a novel Clustered FedStack framework based on the previously published Stacked Federated Learning (FedStack) framework. Here, the local clients send their model predictions and output layer weights to a server, which then builds a robust global model. This global model clusters the local clients based on their output layer weights using a clustering mechanism. We adopt three clustering mechanisms, namely K-Means, Agglomerative, and Gaussian Mixture Models, into the framework and evaluate their performance. Bayesian Information Criterion (BIC) is used with the maximum likelihood function to determine the number of clusters. Our results show that Clustered FedStack models outperform baseline models with clustering mechanisms. To estimate the convergence of our proposed framework, we use Cyclical learning rates.

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN ( RepDRL-DDQN ) and Dueling DDQN ( RepDRL-DDDQN ) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery.

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

With an innovation to offer in product & services in the Banking and Financial Services Industry, FinTech has received both attention from investors and regulatory bodies across globe. With a primary objective to improve financial inclusion for unbanked population in emerging economies through mobile devices, Fintech have become new commercial entities that promise to deliver millennium goals of World Bank and G20 nations. The availability of huge user data due to use of mobile devices and its use to generate sales of financial products, FinTech companies and solutions are constantly changing and are unique to each Fintech company. Fintech have devised a business model to provide financial services in the form of payment services, wealth management, crowd funding, lending services, forex services for remittance, digital wallets and digital currencies. The data driven business model, connected customers over mobile phones and several financial services demand new regulatory framework that also protects consumers and prevents systemic risk in the economy. In this paper, we have identified the regulatory guidelines issued for various financial product for customers and challenges Fintech will need to solve to provide innovative services. The paper aims to cover the life cycle of products, scope of innovation for Fintech and methods to meet requirements by regulatory bodies. This paper covers the requirements by products in “Account Services” offered by Banking and Financial Services Industry.

Banking and Financial Services Corporations operating on cloud infrastructure is constantly upgrading the application and cloud technologies based on business requirements and new features available. With IaaS and PaaS platforms being managed and upgraded by cloud services providers, the SaaS platform is operated, monitored and governed by bank's technology department. SaaS continues to be the platform under constant upgrade and development considering the need and change in business scenarios. SaaS platform in cloud infrastructure also acts as a channel to enable banking business with new tools and techniques, keeping existing infrastructure capability & scalability within compliance framework. SaaS platform hosts critical applications required for every operations of Bank which includes core banking application, internet banking platform, Card processing applications, ATM & EFTPOS networks and security monitoring tools. Considering the business criticality and application value, SaaS platform is configured to avoid business disruptions. In this paper we have discussed the challenges in deploying the changes required in applications over SaaS platform and methods to mitigate those challenges with minimum or no disruption to cloud architecture infrastructure.

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence, and it exploits how to use artificial intelligence to enhance big data analytics for various applications.1 As cloud computing cannot meet the strict computing time requirement in latency-critical big data analysis applications, edge computing has emerged as a solution to address the drawbacks of cloud-based solutions by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex intelligent big data analytics tasks. Consequently, this special issue is focused on exploiting key techniques of intelligent big data analytics by involving cloud and edge computing. Presented with an avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and requires more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. For this reason, Chen et al2 proposed APEX2S, a novel two-layer machine learning model, for discovery of the protein-protein interactions data. APEX2S calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply machine learning techniques for learning the interactions data and making predictions. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human-computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. To solve this, Zhao et al3 proposed a novel action recognition method to improve the recognition accuracy by adopting the key frame extraction and multi-feature fusion techniques. A key frame extraction method based on node contribution weighting is proposed to extract video key frames, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Many applications are now deployed on Virtual Machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval-priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to over controls. Fluctuating prices make Spot VMs unreliable due to unexpected termination which makes fault-tolerant strategies crucial. In order to decrease the VM rental cost while guaranteeing the SLA and robustness, Cai et al4 proposed a hybrid control method UCM which takes advantage of queuing-model-based loosely coupled controllers, unequal-interval-based collaborating method, and an existing group-based fault tolerant strategy. Lidar-based city objects detection is an interesting topic along with the development of Laser scan equipment which has been widely applied in various applications such as 3D building reconstruction, navigation, and so on. Superpixel segmentations are widely applied to image processing or computer vision tasks. Many experiments have proven that superpixels generated from atomic meaningful pixel regions, can improve the processing efficiency while losing little information of the original image. Therefore, Mao et al5 describes a city object detection algorithm for airborne Lidar images using superpixel segmentation and DenseNet classification. A three-block DenseNet is applied to classify the superpixels into four main types of city objects (Building, road, field, and railway). In addition, a graph based neighborhood adjustment algorithm is designed to further improve the classification results. Virtual network embedding (VNE) aims to solve how to efficiently allocate physical resources to a virtual network. However, this issue has been proved to be an NP-hard problem. To address the challenge, Wang et al6 formalize the problem as a mixed integer programming problem and propose a novel VNE method based on reinforcement learning. Then to solve this problem, Wang et al6 introduce a pointer network to generate virtual node mapping strategies through an attention mechanism, and design a reward function related to link resource consumption to build the connection between node mapping and link mapping stages of VNE. Dual-hop 60 GHz wireless networks which support relay-assisted dual-hop transmission have been widely adopted in recent years, aiming to prolong communication distance and bypass obstacles in 60 GHz band. However, it is very challenging to perform link scheduling in such dual-hop architecture while considering several factors, that is, reducing network power consumption, avoiding overloaded APs/relays and adapting to network dynamics. To this end, Wu et al7 investigate the problem of energy efficient link scheduling with load constraints (ELL), and propose solutions to deal with network dynamics by presenting a fine-grained energy model for dual-hop 60 GHz networks and proposing a polynomial-time global scheduling algorithm. Job-pool based workload estimation has attract a lot of attention recently, which analyzes the characteristics of existing tasks' workloads to estimate the currently running tasks' workload. However, the workload patterns of some tasks do have seasonality and trend, and conventional per-job based regression methods may yield better workload prediction results. Also, in some cases, some new tasks may not follow the workload patterns of existing tasks in the pool. Thus, Yu et al8 develop an integrated scheme which combines clustering and regression for workload prediction. Exorbitant resources are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though; on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter-GPU's communications during model training, and multi-GPU systems lead to complex connectivity. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. To contend with this, Zhang et al9 introduce two execution optimization methods based on pipeline-hybrid parallelism in a GPU cluster with heterogeneous networking. Sketch is a compact data structure used to summarize data streams. It is widely used in the measurement of network traffic, and its accuracy is higher than traditional methods. Currently, there are some typical sketches: Count-Min Sketch, CU Sketch, and Count Sketch. According to the characteristics of network traffic, Zhu et al10 propose a new sketch framework called Self-Adaption Sketch, which combines Sketch and Bloom Filter. In the framework, the sketch is created dynamically and the memory space is adjusted timely according to the network traffic by using the concept carrying. We thank the authors for their contributions, including those whose papers are not included in this special issue. We also would like to acknowledge thoughtful work from many reviewers who provided valuable evaluations and recommendations.

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.

Cloud architecture infrastructure for Banking and Financial Services Corporations operates on access control mechanism and trust over various cloud user. Having established the on demand service delivery model to operate, maintain, control and govern the cloud architecture, Banking and Financial Services Corporations deliver round the clock services to customers. Despite stringent security controls and minimum access level, the cloud users identify and exploit the cloud vulnerabilities. This leads to data leakages and loss of critical information. Depending upon the area of business and sophistication with which vulnerabilities have been exploited, frauds are committed and system are damaged by employees and third party contractors. The reason for such exploitation ranges from disgruntlement to personal issues of employees and third party contractors. In this paper, we have identified the various types of insiders, their motives, risk associated with information systems and methods of mitigation in Banking and Financial Services Corporations.

Information systems over cloud architecture infrastructure for Banking and Financial Services Corporations are under constant monitoring by IT security team. This monitoring is to keep check for vulnerabilities, security breach incidents and intrusion in cloud environments and network. IT security team are equipped with sophisticated monitoring tools and empowered to take action in the event of security breach or cyber-attack. This gives IT security team privilege access rights to block and investigate attachments, data packets and logs of various users of cloud infrastructure. The investigation may involve or provide access to the private information of the users. This information can be in emails, files & folders, personal documents and messenger history. In such events, IT security teams are supposed to follow ethical standards established by organization and show high integrity to keep privacy of the cloud users and confidentiality of the investigation. In this paper, we have identified the roles & responsibilities and ethical behaviour expected from IT security teams. This paper also enlists various events that demand ethical behaviour from IT security teams in Banking and Financial Services Corporations.

Cloud computing architecture and infrastructure has received an acceptance from corporations and governments across the globe. Cloud computing helped to reduce cost of management of physical and technical infrastructure at the same time has made information systems available for locally & globally deployed work force. Cloud computing infrastructure provides access to data and applications from any location and this has made organizations to keep evaluating privacy and security framework. Banking and financial services have data and applications which are internally developed to remain ahead of competition. This data and applications becomes the Intellectual Property (IP) that serves specific business processes and goals. When this data and applications can be accessed from remote locations, there may be a potential risks of data leakages and erosion of IP over a period of time. With an adoption of cloud computing, banking and financial services industry continues to be under strict regulatory and compliance framework to maintain privacy of data and security of systems. Privacy and security of cloud architecture infrastructure continues to be the challenge across the globe. In this paper, various aspects of cloud computing related to data privacy and system security for banking and financial services industry have been introduced.

Emerging topic detection from microblogs has developed into an attractive task because events usually break on social channels. However, due to the features of high noise, short length, fast arriving rate and irregular writing style of microblogs, it has been proven to be a challenge to detect emerging topics from microblog streams early and accurately in a scalable way. Several approaches have been proposed to tackle this problem and have achieved sound performance in some aspects. However, from the point of novelty and scalability, there is still considerable space for improvement. Inspired by the consideration, we propose an emerging topic detection framework based on emerging pattern mining. Via encoding the term novelty into an efficient high utility itemset mining (HUIM) algorithm, a group of emerging patterns which are concise and interpretive representations of topics can be first detected, decreasing the computational cost of the clustering part.

Learning low dimensional representations from a large number of short corpora has a profound practical significance but with vital challenge in content analysis and data mining applications. In this paper, we propose a novel topic model called Block Bayesian Sparse Topic Coding (Block-BSTC), which is capable of discovering the latent semantic representation of short texts. The Block-BSTC relaxes the normalization constraint of the inferred representations with word embeddings and block sparse Bayesian learning, which is convenient to directly control the sparsity of word codes with exploiting the intra-block correlations. Furthermore, the experimental results show that Block-BSTC achieves great performance on the sparsity ratio of word codes. Meanwhile, it can improve the accuracy of document classification.

Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

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.

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.

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.

Due to a demand for computing of huge proportions and cheap in cost, cloud computing is now a favoured model. This type of technology has been utilized by some government organizations to address the demands and requirements of their citizen. However, constraints exist with the use of this model impacting on service provision and use of this technology. This paper will explore the disparate challenges and issues that impacting on the use of cloud computing in Australian local councils. 480 IT staff from 47 local government councils was investigated. The findings will aid authorities to analyse the effectiveness of endorsing cloud computing and develop their understanding of the problematic components this may entail. This research comprises a series of findings on cloud computing with specific focus in this paper on different challenges and issues surrounding the adoption of this technology by Australian local councils.

The terminology of cloud computing defines a network, which shares applications, processing power and large systems among many other computers. It can be a foundation for media channels and other applications. Cloud computing provides great opportunity for each practicing individuals to connect and extend their local computing capabilities over the global processing power. Advanced means of communication are one of the products of such computing. Cloud computing may function as a single-function application, a platform on which these applications can work, a group of services that provide numerous advantageous computing resources, and the potential to save a great quantity of data. The incorporation of cloud computing and the media processing is therefore quit natural selection for both of the areas, and therefore comes out the media cloud.

Sensor networks usually generate mass of data, which if not structured for future applications, will require much effort on analytical processing and interpretations. Thus, storing sensor data in an effective and structured format is a key issue in the area of sensor networks. In the meantime, even a little improvement on data storing structure may lead to a significant effect on the lifetime and performance of the sensor network. This paper describes a new method for sensor storage that combines semantic web concepts, a data aggregation method along with aligning sensors in hierarchical form. This solution is able to reduce the amount of data stored at the sink nodes significantly. At the same time, the method structures sensed data in a way that we can respond to semantic web-based queries with less consumption of energy compared to previous conventional methods. Results show that, in some situations especially when the diversity of query responses and life of network are vital, the efficiency of our new solution is much better.

Provenance, describes how a data item came to be its current state. A Directed Acyclic Graph, nature of the provenance poses challenges to access control models and querying languages. Access control policies are needed to protect the security of data provenance. In this paper, we apply proposed notions of regular expression for provenance as the structure of a provenance graph is very large. We use an extending access control language for data provenance, and semantic Web technologies to effectively query provenances. Finally, we tailored an access control language by using the regular expressions through a medical example.

This paper presents a High Availability Application Service Platform (HAASP) and analyses its high availability. The architecture of this platform contains application server platform and high availability architecture. Application server platform consists of three major components, application release platform, which is implemented with WebLogic and WebSphere cluster, application and database platforms, which are built with Oracle RAC, and IBM Domino cluster. High availability architecture is constructed by virtual machines, distributed switches and storage pools. It employs virtualization technology to integrate physical servers and storages, where physical servers are responsible for the creation of virtual server cluster, distributed switches, storage pool and management of storages. Application server platform runs on the high availability architecture and our experiment result shows that HAASP, as a scalable and flexible architecture, could offer more persistent service compared with traditional application service platforms.

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.

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 thro ugh 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.

Cloud computing is seen as an increasingly important enabler for improving productivity, efficiency and cost reduction. This research aims to identify factors that are perceived likely to influence the adoption of cloud computing for Australian municipal governments. The research model draws from the technology-organization-environment (TOE) framework and the diffusion of innovation (DOI) model. The research employed in-depth interviews of Australian councils' IT managers. The main factors that were identified as playing a significant role in Australian local council's adoption of cloud services were: relative advantage, compatibility, cost, technology readiness, competitive pressure.

Open Provenance Model (OPM) is a provenance model that can capture provenance data in terms of causal dependencies among the provenance data model components. Causal dependencies are relationships between an event (the cause) and a second event (the effect), where the second event is understood as a physical consequence of the first. Causal dependencies can represent a set of entities that are necessary and sufficient to explain the presence of another entity. A provenance model is able to describe the provenance of any data at an abstract layer, but does not explicitly capture causal dependencies that are a vital challenge since the lacks of the relations in OPM, especially in healthcare environment. In this paper, we analyse the causal dependencies between entities in a medical workflow system with OPM graphs.

Recently the massive open online course (MOOC) is an emerging trend that attracts many educators' and researchers' attentions. Based on our pilot study focusing on the development and operation of MOOC in Australia, we found MOOC is featured with mastery learning and blended learning, but it suffers from low completion rates. Brining micro learning into MOOC can be a feasible solution to improve current MOOC delivery and learning experience. We design a system which aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To investigate how micro learning can impact learning experience and knowledge acquisitions of learners participated in MOOC, we suggest a potential scheme including hypotheses to evaluate our proposed approach.

Motivated by applications in social networks, a new type of dominating set named Positive Influence Dominating Set (PIDS) has been studied in the literature. In this paper, we investigate cooperative cost games arising from PIDS problem on social network graphs. We propose two new game models, Rigid PIDS Game and Relaxed PIDS Game, and focus on their cores. First, a relationship between the cores of both games is obtained. Next, we also prove that the core of the relaxed PIDS game is nonempty if and only if there is no integrality gap for the relaxation linear programming of the PIDS problem on graph G.

Even though we are in the information age, we do not have quantitative, objective tools to evaluate organisational information systems. This paper contributes a fundamental theory: information systems balance theory for this purpose. We design and develop a theoretic framework based on an economic model. We use this robust economic model to explain the value chain of designated information systems. Through finding the information systems balance point, we identify the comfort zone by calculating the average ratio of the notation value (V) of designated information systems and its organisational revenue for a certain period of time.

Enhancing teamwork performance is a significant issue in mobile cloud-based learning. We introduce a service oriented system, Teamwork as a Service (TaaS), to realize a new approach for enhancing teamwork performance in the mobile cloud environment. To coordinate most learners' talents and give them more motivation, an appropriate task allocation is necessary. Utilizing the Kolb's learning style (KLS) to refine learner's capabilities, and combining their preferences and tasks' difficulties, we formally describe this problem as a constraint optimization model. Two heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA), are employed to tackle the teamwork-enhanced task allocation, and their performances are compared respectively. Having faster running speed, the SA is recommended to be adopted in the real implementation of TaaS and future development.

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.

Conditional functional dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. This search space has been used in the currently most efficient constant CFD discovery algorithm. In this paper, we propose pruning criteria to further prune the theoretic search space, and design a fast algorithm for constant CFD discovery. We evaluate the proposed algorithm on a number of media to large real-world data sets. The proposed algorithm is faster than the currently most efficient constant CFD discovery algorithm, and has linear time performance in the size of a data set.

Advances in technology offers new opportunities in enhancing teaching and learning. The new technologies enable individuals to personalize the environment in which they work or learn, a range of tools to meet their interests and needs. In this paper, we try to explore the salient features of the nature and educational potential of `cloud computing' (CC) in order to exploit the affordance of CC in teaching and learning in a higher education context. It is evident that cloud computing has a significant place in the higher education landscape both as a ubiquitous computing tool and a powerful platform. Although, the adoption of cloud computing promises various benefits to an organization, a successful adoption of cloud computing in an organization, particularly in educational institutes requires an understanding of different dynamics and expertise in diverse domains. This paper aims at a roadmap of Cloud Computing for Higher Education (CCHE) which provides with a number of steps for adopting cloud computing.

With the increased development of cloud computing, access control policies have become an important issue in the security filed of cloud computing. Semantic web is the extension of current Web which aims at automation, integration and reuse of data among different web applications such as clouding computing. However, Semantic web applications pose some new requirements for security mechanisms especially in the access control models. In this paper, we analyse existing access control methods and present a semantic based access control model which considers semantic relations among different entities in cloud computing environment. We have enriched the research for semantic web technology with role-based access control that is able to be applied in the field of medical information system or e-Healthcare system. This work demonstrates how the semantic web technology provides efficient solutions for the management of complex and distributed data in heterogeneous systems, and it can be used in the medical information systems as well.

Web service composition has become a promising technology in a variety of e-science or e-business areas. There are a variety of models and methods to deal with this issue from different aspects. Bio-inspired algorithms are becoming main approaches and solutions. This paper reviews the current researches on web service composition based on bio-inspired algorithms, such as Ant Colony Optimization (ACO), Genetic Algorithm(GA), Evolutionary Algorithm (EA) and Particle Swarm Optimization(PSO). By analyzing and investigating different approaches, this paper gives an overview about the researches on bio-inspired algorithm in web service composition and point out future directions.

Ontology is specification of shared conceptualization and is building block of the semantic Web. Ontology building requires a detailed domain analysis that in turn requires financial resources, intensive domain knowledge and time. Most of industrial data is present in relational databases and a relational database schema represents a domain model. An ontology built from this schema can represent concepts and relationships that are present in domain of discourse. However, databases are not static and their schema evolves over time. Once a database schema is changed, these changes in schema should also be incorporated in ontology, generated from this database. The possible solution of regenerating a new ontology from changed database schema is not feasible because this will result in loss of manual changes of ontology. In this paper we present an approach that can be used to generate ontology from RDBs and to synchronize the generated ontology with changes occurred in the same database. We also present the prototypical implementation of the proposed approach as Protégé plug-in (i.e. DATAONTO) that can be used to generate ontology from database and to synchronize the ontology with the original database.

This paper presents a deduplication storage system over cloud computing. Our deduplication storage system consists of two major components, a front-end deduplication application and Hadoop Distributed File System. Hadoop Distributed File System is common back-end distribution file system, which is used with a Hadoop database. We use Hadoop Distributed File System to build up a mass storage system and use a Hadoop database to build up a fast indexing system. With the deduplication applications, 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 cloud storage system is more efficient than traditional deduplication approaches.

Identity fraud has become one of major concerns for broad communities. The new information era needs a new digital identity infrastructure to support next generation Internet. This article suggests a hierarchical structure for digital identities. We define and classify all digital identities into three broad categories: Object, People and Organization. This paper is the first to systematically address the classification of digital identities. More and more individuals and communities heavily rely on the network. Many countries are trying their own digital identity initiatives, like E-passport, national smart card, etc. This article intends to initiate a discussion on a universal digital identity infrastructure for our future. We believe that in the near future all paper-based identities will be replaced by digital identities. A robust digital identity infrastructure will take a vital role in the future information age.

XACML and its reference implementation can not directly support quantified risk adaptive access control, because there are several special requirements to specify and enforce the policies in risk adaptive access control: the elements in these policies, such as risk, risk level, are not covered; and risk in quantified risk adaptive access control would be mutable, accumulated and required to be continuously controlled. This paper, therefore, extends XACML and its reference implementation to support quantified risk adaptive access control. This paper makes two contributions: design a risk adaptive policy language extended from XACML; and propose a framework to enforce the policies. To the best of our knowledge, this paper is the first research work to discuss this topic.

This paper contributes to build a quantitative approach for digital identity assurance. We propose to use a hierarchical structure level to define the assurance level, taking weighted affiliation information into consideration for assurance level; thus creating a more practical assurance supporting model is designed for any digital identities. In doing this an accurate mathematical framework is constructed to calculate the exact assurance level for any digital identities. The paper contributes a concrete architecture of registration authority of digital identity associated with a hierarchical domain name structure for registration authority. The mechanism of assurance support for digital identities can be broadly applied in any federated digital identity management systems.

This paper focuses on the challenges on the privacy of Radio Frequency Identification (RFID) systems. RFID systems have already widely applied in industry and have been bringing lots of benefits to our daily life, it also creates new security and privacy problems to individuals and organizations. The security and privacy challenges are analysed after a brief introduction of various RFID systems and their associated operations. A proposal to protecting security and privacy of customers, as a solution of the challenge for low-cost RFID systems is designed. Finally, comparisons to related works of the proposal are described.

Role-based access control (RBAC) has been proven to be a flexible and useful access control model for information sharing in distributed collaborative environments. Permission-role assignments (PRA) is one important process in the access model. However, problems may arise during the procedures of PRA Conflicting permissions may assign to one role, and as a result, the role with the permissions can derive unexpected access capabilities. This paper aims to analyze the problems during the procedures of permission-role assignments in distributed collaborative environments and to develop authorization allocation algorithms to address the problems within permission-role assignments. The algorithms are extended to the case of PRA with the mobility of permission-role relationship. Finally, comparisons with other related work are discussed to demonstrate the effective work of the paper.

This paper systematically addresses the security and privacy concerns for e-learning systems. An effective architecture of e-learning system is proposed for a thorough overview on security and privacy issues related to current e-learning systems. This paper further examines the relationship among security & privacy policy, available security & privacy technolongy, and the degree of e-learning privacy & security. This paper significantly contributes to the knowledge of e-learning security & privacy research communities and will generate more research interests in this regard.

This paper tries to address the security issue which e-learning systems are facing. Through well designed meta-formats of digital identities, the privacy of e-learning users can be well preserved.

WFMSs are widely used by modern business processes. But so far it is still a challenge to have a simple and effective access control mechanism for WFMSs. This paper contributes an effective and simply access control mechanism, called neighbourhood-trust dependency access control (NETDEPAL), for WFMSs. This new notion combines the workflow mechanism and RBAC into NETDEPAL for WFMSs. The secure access for WFMSs can be efficiently implemented by NETDEPAL from system dependency and task dependency via their neighbourhood relationships

As more and more organisations and institutions are moving towards the e-learning strategy, more and more disparate data are distributed by different e-learning systems. How to effectively use this vast amount of distributed data becomes a big challenge. This paper addresses this challenge and works out a new mechanism to implement data integration for e-learning. A workflow management system based (WFMS-based) data integration model is contributed to the e-learning

E-learning has become one of most important means for the future education, especially for universities. This paper is based on our e-learning teaching experience which comes from one of the leading e-learning university, the University of Southern Queensland. We use the workflow mechanism to analyse the e-learning system. The whole e-learning system is divided into four sub-workflow systems, teaching sub-workflow, learning sub-workflow, admin sub-workflow and infrastructure sub-workflow. Through a coherent analyse of co-relationship of main activities in four sub-workflow systems, some activities are identified as the key elements for the e-learning system. By enhancing these key elements, the performance of all the e-learning workflow gets a significant improvement and e-learning students get better grades than on-campus and traditional distance students. The workflow-based e-learning system can effectively reach the expected achievement of e-learning.