Description:
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.
Description:
Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modeling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a `ground mask'. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines extracted from the grey-scale version of the orthoimage are classified into several classes such as `ground', `tree', `roof edge' and `roof ridge' using the ground mask and colour and texture information from the orthoimagery. During roof plane extraction the lines from the later two classes are used to fit roof planes to the neighbouring non-ground LIDAR points. Finally, a new rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method successfully removes vegetation and offers high extraction rates.
Description:
Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modeling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a `ground mask'. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines extracted from the grey-scale version of the orthoimage are classified into several classes such as `ground', `tree', `roof edge' and `roof ridge' using the ground mask and colour and texture information from the orthoimagery. During roof plane extraction the lines from the later two classes are used to fit roof planes to the neighbouring non-ground LIDAR points. Finally, a new rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method successfully removes vegetation and offers high extraction rates.
Description:
Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modelling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a ‘ground mask’. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines are extracted from the grey-scale version of the orthoimage and then classified into several classes such as ‘ground’, ‘tree’, ‘roof edge’ and ‘roof ridge’ using the ground mask and colour and texture information from the orthoimagery. During segmentation of the non-ground LIDAR points, the lines from the latter two classes are used as baselines to locate the nearby LIDAR points of the neighbouring planes. For each plane a robust seed region is thereby defined using the nearby non-ground LIDAR points of a baseline and this region is iteratively grown to extract the complete roof plane. Finally, a newly proposed rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method can successfully remove vegetation and so offers high extraction rates.
Description:
SERVQUAL as a model has for long attracted researchers to apply in different contexts. The objective of this research is to focus on e-service quality in the absence of face-to-face encounter for commercial banks in an emerging economy such as Namibia. The focus is to understand both customer perception about Internet banking and the usability of the banking website. Based on prior literature on service quality assessment and website usability a priory model was developed. The model is then tested through a questionnaire survey of customers of commercial banks in Namibia. Using factor analysis, a refined model for assessing service quality of Internet banking was developed. The refined model includes three service quality dimensions: service performance, communication and website design. Service performance was most preferred on satisfaction level whereas in communication customers were dissatisfied as well as indifferent towards a 24 hours customer's service. This is in contrary to the existing literature. The third dimension, i.e. website design was high on customer satisfaction level. Finally, on usability evaluation of banking website it was found that acceptance level of the Internet banking website for the commercial banks in Namibia is marginally high.
Description:
SERVQUAL as a model has for long attracted researchers to apply in different contexts. The objective of this research is to focus on e-service quality in the absence of face-to-face encounter for commercial banks in an emerging economy such as Namibia. The focus is to understand both customer perception about Internet banking and the usability of the banking website. Based on prior literature on service quality assessment and website usability a priory model was developed. The model is then tested through a questionnaire survey of customers of commercial banks in Namibia. Using factor analysis, a refined model for assessing service quality of Internet banking was developed. The refined model includes three service quality dimensions: service performance, communication and website design. Service performance was most preferred on satisfaction level whereas in communication customers were dissatisfied as well as indifferent towards a 24 hours customer's service. This is in contrary to the existing literature. The third dimension, i.e. website design was high on customer satisfaction level. Finally, on usability evaluation of banking website it was found that acceptance level of the Internet banking website for the commercial banks in Namibia is marginally high.
Description:
In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
Description:
In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
Description:
Whether employee unions have a role to play in Universities (and what that role might be) has always been troubling, with views ranging from philosophical antipathy to wholesale endorsement. In some jurisdictions, levels of membership and support also have varied in response to such external factors as statutory changes to governance structures of universities, voluntary unionism, changes to the direct role of unions in furthering the statutory responsibilities of employers such as in promotion of health and safety in the workplace, and such internal factors as casualisation, centralisation of power and decision making and policies of workforce flexibility via redundancy and other strategies. An emerging “threat” that is garnering increasing levels of concern is that of automation in the higher education sphere, not only in ancillary functions such as learning management systems (LMS), information processing and provision of student support services, but also going to the root of the academic function – the education experience. This development will have fundamental implications for higher education, as for other service industries, and poses essential challenges for employee unions in terms of their relevance, governance and leadership, particularly where that automation threatens job security and careers. This paper will explore those challenges by reference to analogous developments such as MOOCs, “cookie-cutter” courses and programs, casualisation and the growth in on-line, flexible and blended delivery modes. As a review and commentary, the exploration will focus on the Australian context but to ensure broader relevance, will be grounded in political economy, reflecting the tensions that emerge between the funders of universities, both public and private, who seek higher profits, control and power, and those who see universities as a fundamental social institution. Arguably, it is in the space created by such tensions that the future for unions is located. It is a matter of determining what that future looks like. This approach permits both contextualisation of the discussion and provides opportunities for international comparisons, thereby providing a basis for future research within the context of academic leadership.
Description:
Abstract Background Although effective treatments exist for diagnostic and subthreshold-level eating disorders (EDs), a significant proportion of affected individuals do not receive help. Interventions translated for delivery through smartphone apps may be one solution towards reducing this treatment gap. However, evidence for the efficacy of smartphones apps for EDs is lacking. We developed a smartphone app based on the principles and techniques of transdiagnostic cognitive-behavioral therapy for EDs and evaluated it through a pre-registered randomized controlled trial. Methods Symptomatic individuals (those who reported the presence of binge eating) were randomly assigned to the app ( n = 197) or waiting list ( n = 195). Of the total sample, 42 and 31% exhibited diagnostic-level bulimia nervosa and binge-eating disorder symptoms, respectively. Assessments took place at baseline, 4 weeks, and 8 weeks post-randomization. Analyses were intention-to-treat. The primary outcome was global levels of ED psychopathology. Secondary outcomes were other ED symptoms, impairment, and distress. Results Intervention participants reported greater reductions in global ED psychopathology than the control group at post-test ( d = −0.80). Significant effects were also observed for secondary outcomes ( d 's = −0.30 to −0.74), except compensatory behavior frequency. Symptom levels remained stable at follow-up. Participants were largely satisfied with the app, although the overall post-test attrition rate was 35%. Conclusion Findings highlight the potential for this app to serve as a cost-effective and easily accessible intervention for those who cannot receive standard treatment. The capacity for apps to be flexibly integrated within current models of mental health care delivery may prove vital for addressing the unmet needs of people with EDs.
Description:
Insecurity is no longer a condition that is specific to a set class of workers it has become a global issue. It can affect workers of any age, gender or ethnicity, across industrial or service sectors, and even our universities are no longer safe from casualisation, underemployment and outsourcing. To be sure, employment has always had elements of precariousness associated with it, particularly in the primary sectors. However, the steady intensification of capitalism around the world, and its intrusion into almost every facet of human endeavour, has meant that precarious work, as Arne Kalleberg writes, 'has become much more pervasive and generalized: [even] professional and managerial jobs are also precarious these days'. The telltale signs of precarity, such as insecurity, uncertainty and atomisation/individualisation, are frequently associated with the neoliberalisation of global societies. The twin mantras of flexibility and mobility across the economic market are further evidence of neoliberal principles, as an increasingly casualised work force allows employers the flexibility to shed and acquire labourers in accordance with the demands of capital rather than any humanitarian concern.
Description:
Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of under-segmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.
Description:
Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of under-segmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.
Description:
Management of maritime military platform life is a real challenge for operators and maintainers. One of the major problems is how to capture cost effective information about the hull defect and predict the remaining life? This paper will focus on the automation process for calculation of the failure rate and prediction from collated hull defects information. This data is assessed for accuracy, sensitivity and correctness of defect location and failure modes. An outline of the automation techniques investigated to determine the hull failures rates is discussed. Five major failure modes are analysed and a high level approach for integrating failure data collation is presented. The paper then discusses some of the issues and challenges with obtaining reliable maintenance data and opportunities for further research in finding a better solution.
Description:
The use of Multimedia Wireless Sensor Networks (MWSNs) is becoming common nowadays with a rapid growth in communication facilities. Similar to any other WSNs, these networks face various challenges while providing security, trust and privacy for user data. Provisioning of the aforementioned services become an uphill task especially while dealing with real-time streaming data. These networks operates with resource-constrained sensor nodes for days, months and even years depending on the nature of an application. The resource-constrained nature of these networks makes it difficult for the nodes to tackle real-time data in mission-critical applications such as military surveillance, forest fire monitoring, health-care and industrial automation. For a secured MWSN, the transmission and processing of streaming data needs to be explored deeply. The conventional data authentication schemes are not suitable for MWSNs due to the limitations imposed on sensor nodes in terms of battery power, computation, available bandwidth and storage. In this paper, we propose a novel quality-driven clustering-based technique for authenticating streaming data in MWSNs. Nodes with maximum energy are selected as Cluster Heads (CHs). The CHs collect data from member nodes and forward it to the Base Station (BS), thus preventing member nodes with low energy from dying soon and increasing life span of the underlying network. The proposed approach not only authenticates the streaming data but also maintains the quality of transmitted data. The proposed data authentication scheme coupled with an Error Concealment technique provides an energy-efficient and distortion-free real-time data streaming. The proposed scheme is compared with an unsupervised resources scenario. The simulation results demonstrate better network lifetime along with 21.34 dB gain in Peak Signal-to-Noise Ratio (PSNR) of received video data streams.
Description:
This book comprises the select proceedings of the 3rd Biennial International Conference on Future Learning Aspects of Mechanical Engineering (FLAME) 2022. It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in industrial and production engineering. Various topics covered include sustainable manufacturing processes, logistics and supply chains, Industry 4.0 practices, circular economy, lean six sigma, agile manufacturing, additive manufacturing, IoT and Big Data in manufacturing, 3D printing, simulation, manufacturing management and automation, surface roughness, multi-objective optimization and modelling for production processes, developments in casting, welding, machining, and machine tools and many more advancements in industrial and production engineering. This volume will prove a valuable resource for those in academia and industry working in the area of industrial and production engineering.