The exposure of business applications to the web has considerably increased the variability of its workload patterns and volumes as the number of users/customers often grows and shrinks at various rates and times. Such application characteristics have increasingly demanded the need for flexible yet inexpensive computing infrastructure to accommodate variable workloads. The on-demand and per-use cloud computing model, specifically that of public Cloud Infrastructure Service Offerings (CISOs), has quickly evolved and adopted by majority of hardware and software computing companies with the promise of provisioning utility-like computing resources at massive economies of scale. However, deploying business applications on public cloud infrastructure does not lead to achieving desired economics and elasticity gains, and some challenges block the way for realizing its real benefits. These challenges are due to multiple differences between CISOs and application's requirements and characteristics. This article introduces a detailed analysis and discussion of the economics and elasticity challenges of business applications to be deployed and operate on public cloud infrastructure. This includes analysis of various aspects of public CISOs, modeling and measuring CISOs' economics and elasticity, application workload patterns and its impact on achieving elasticity and economics, economics-driven elasticity decisions and policies, and SLA-driven monitoring and elasticity of cloud-based business applications. The analysis and discussion are supported with motivating scenarios for cloud-based business applications. The paper provides a multi-lenses overview that can help cloud consumers and potential business application's owners to understand, analyze, and evaluate important economics and elasticity capabilities of different CISOs and its suitability for meeting their business application's requirements.

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WS-Policy4MASC is an XML language for specification of policies for run-timeWeb service management (monitoring and control) activities executed by the Manageable and Adaptable Services Compositions (MASC) middleware. Among its original contributions are specification of diverse business values (benefits or costs, tangible or intangible, agreed or possible, absolute or relative) and specification of various control strategies maximizing different business values (e.g., only agreed intangible benefits). To facilitate development of Web service systems that can be managed with WS-Policy4MASC and the MASC middleware and to improve alignment between run-time management activities and design-time models, we developed novel UML profiles for WS-Policy4MASC. Their original contributions are in improved support for: a) specification of run-time management activities and business values within design-time models, b) automatic creation of run-time management policies from designtime models, c) feedback of run-time management information values into analysis of design-time models. We validated our solutions on detailed examples.

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With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

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Data-driven machine learning models, compared to numerical models, shown promising improvements in detecting damage in Structural Health Monitoring (SHM) applications. In data-driven approaches, sensors' data are used to train a model either in a centralized server or locally inside each sensor unit node (decentralized model similar to edge computing). The centralize learning model suffers from issues including wireless transmission costs and data sensitive data vulnerability. The decentralized model also poses different challenges such as feature correlations and relationships loss in decentralized learning. To handle the shortcomings of both models, we proposes a new Federated Learning model augmented with tensor data fusion to detect damage in SHM. Our approach enables the central machine learning model to gain experience from diverse datasets located at different sensor locations. It also trains a shared centralized machine learning model using datasets stored and distributed across multiple sensor nodes. Our experimental results on real structural datasets demonstrate promising damage detection accuracy without the need to transmit the actual data to the centralized learning model. It also shows that the data correlations and relationship from all participating sensors are preserved.