BUSINESS-IT ALIGNMENT, THE STRUGGLE CONTINUES

Business and IT alignment continues to be a challenge for business which seek to maximise value of and from the IT function. Research has covered the wider Business-IT alignment points from mainly a macro-level viewpoint, such as, structural, dynamic and functional alignment. However, both research and practice still consider Business and IT alignment to be a challenge. In this research, we seek to uncover part of the reasons why Business and IT alignment is challenging to organisations. We note the factors in the literature which emphasise Business-IT alignment, such as, shared understanding, communication, management commitment, IT investment evaluation, innovation and rewards, strategic planning of IS, and strategic agility. The results of this study show that even if organisations address these alignment factors, IT projects could still end up failing. We also note the opposing misalignment factors in practice, such as, human tensions and strained work relationships, knowledge silos, self-centred management, technology does not matter, organisational change resistance, technology as a burden, and resources inflexibility. We conclude that organisations need to address both alignment and misalignment factors

MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users’ tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRF

Internet of Things for Education: Facilitating Personalised Education from a University’s Perspective

Personalised education has been a developmental goal across all levels of the UK education sector for many years. In particular, the Higher Education sector has struggled the most due to a lack of personalisation, as student numbers in lecture theatres have grown significantly, occasionally exceeding three hundred. As a consequence, educators are constantly challenged to gather and understand individual student needs, let alone address them. At the same time, technology has advanced in the recent years, particularly in the areas of Internet of Things (IoT) and big data. IoT technology has emerged as a great means to collect data from lecture theatres and labs, while big data technologies enable the processing of these data. Consequently, IoT offers potential solutions to some of the key issues facing the future of personalised education. In this paper, an IoT system is being proposed, which would enable the personalisation of education for large groups of students in lecture theatres and labs. The proposal is derived from a case study based on work which has taken place in a mid-sized UK university

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for some time now. Cloud computing, as a promising technology, offers usage-based payment, ondemand computing resources. However, in the recent decade, the growing complexity of the IT world resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, fair allocation of resources in the cloud is one of the most important aspects of QoS that becomes more interesting especially when many users submit their tasks and requests include multiple resources. Research in this area has been considered since 2012 by introducing Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the resource fair allocation problem in the cloud. Although DRF has some good features in terms of fairness, it has been proven inefficient in some conditions. Remarkably, DRF and other works in its extension are not proven intuitively fair after all. These implementations have been unable to utilize all the resources in the system and more specifically, they leave the system in an imbalanced situation with respect to each specific resource. To tackle those problems, in this paper we propose a novel algorithm namely FFMRA inspired by DRF which allocate resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments show that our proposed method provides approximately 100% utilization of resources and distributes them fairly among the users and meets good fairness properties

SafePASS - Transforming marine accident response

The evacuation of a ship is the last line of defence against human loses in case of emergencies in extreme fire and flooding casualties. Since the establishment of the International Maritime Organisation (IMO), Maritime Safety is its cornerstone with the Safety of Life at Sea Convention (SOLAS) spearheading its relentless efforts to reduce risks to human life at sea. However, the times are changing. On one hand, we have the new opportunities created with the vast technological advances of today. On the other, we are facing new challenges, with the ever-increasing size of the passenger ships and the societal pressure for a continuous improvement of maritime safety. In this respect, the EU-funded Horizon 2020 Research and Innovation Programme project SafePASS, presented herein, aims to radically redefine the evacuation processes, the involved systems and equipment and challenge the international regulations for large passenger ships, in all environments, hazards and weather conditions, independently of the demographic factors. The project consortium, which brings together 15 European partners from industry, academia and classification societies. The SafePASS vision and plan for a safer, faster and smarter ship evacuation involves: i) a holistic and seamless approach to evacuation, addressing all states from alarm to rescue, including the design of the next generation of life-saving appliances and; ii) the integration of ‘smart’ technology and Augmented Reality (AR) applications to provide individual guidance to passengers, regardless of their demographic characteristics or hazard (flooding or fire), towards the optimal route of escape

Designing social networks to combat fear of missing out.

Fear of missing out, (hereafter referred to as FoMO), is increasingly becoming an issue of concern in relation to the use of Social Network Sites (SNSs). Despite its importance, the effects of FoMO continue to receive limited attention, while guidance on how SNSs design is responsible for developing should and, also, combatting it, remains inadequate. In this position paper, we argue that dual responsibility of SNSs design. We report on initial results of a multiphase empirical study which was undertaken to examine the features of social networks that may contribute to triggering FoMO, and to explore how future SNSs can be designed to aid people manage their FoMO. The study involved three focus group sessions and a diary study. We argue that future SNSs shall support interaction styles and protocols and their agreement and adherence processes to enable people prevent and combat FoMO and present styles for doing that

Comparative study on catalytic and non-catalytic pyrolysis of olive mill solid wastes

In this study, catalytic and non-catalytic fast pyrolysis of dried olive husk and olive kernels was carried out. A bubbling fluidised bed reactor was used for the non-catalytic processing of the solid olive wastes. In-situ catalytic upgrading of biomass fast pyrolysis vapours was performed in a fixed bed bench-scale reactor at 500 °C, for catalyst screening purposes. A maximum bio-oil yield of 47.35 wt.% (on dry biomass) was obtained from non-catalytic fast pyrolysis at a reaction temperature of 450 °C, while the bio-oil yield was decreased at 37.14 wt.% when the temperature was increased to 500 °C. In the case of the fixed bed unit tests, the highest liquid (52.66 wt.%) and organics (30.99 wt.%) yield was achieved with the use of the non-catalytic silica sand. Depending on the catalytic material, the liquid yield ranged from 47.03 to 43.96 wt.% the organic yield from 21.15 to 16.34 wt.% on dry biomass. Solid products were increased from 28.23 wt.% for the non-catalytic run to 32.81 wt.% on dry biomass, when MgO (5% Co) was used

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors