The article describes the application of the Smart City concept and the economic opportunities it creates, infrastructure and services, and opportunities to improve governance. The main features of the Smart City concept, development directions and evolution, standards and solutions, factors and obstacles to its implementation have been analyzed by the author. The experience of different countries in the application of digital technologies is discussed. The article provides the scope and structure of the "smart" market, application stages and scenarios. International experience in this field was widely analyzed and examples were given, comparisons were made and suggestions were made. The article talks about smart cities, the construction of which has already begun in Azerbaijan. The application of the smart city concept in Azerbaijan has been studied. It is stressed that the spread of digital technologies for the construction of a smart city in Azerbaijan is a prerequisite. The Network Readiness Index (NRI) identifies the indicators that are holding back Azerbaijan in the ranking for 2021. In our study, based on the growth dynamics for 2017-2020, the forecast was calculated by analyzing the trend in the ICT development index in Azerbaijan for 2022-2025.

With the continuous development of 3D city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of LiDAR and camera in mobile measurement systems provides a new and highly effective solution. The LiDAR can quickly and accurately acquire the 3D spatial coordinates of target objects, while optical imagery contains abundant color information. If the two can be integrated, they can play an important role in multiple fields such as streetscape modeling, archaeology and digital city, etc. Currently, integrated mobile measurement systems commonly require cameras, lasers, POS and IMU, thus the hardware cost is relatively expensive and the system integration is complex. Therefore, in this paper we propose a simple ground mobile measurement system composed of a LiDAR and a GoPro camera without a POS system, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. Normalized Zernike moments are region-based shape descriptors that can reflect the features of images in multiple dimensions even for low-quality video images. The results show that registration based on normalized Zernike moments provides a good result, with an error accuracy of 0.5-1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing adjacent video image relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the SURF algorithm with distance restriction and RANSAC to select corresponding points, which can improve the reliability of the corresponding points. The results indicate that the accuracy of the relative orientation method is high. Moreover, this method can converge to good results for stereo image pairs with large rotation angles and displacement amounts. Therefore, relative orientation based on essential matrix decomposition and nonlinear optimization has good applicability. (4) The video imagery suffers from significant motion blur and boundary distortion. Therefore, a point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. Experimental results show that the coloring accuracy between the video imagery and point cloud data is high, meeting the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

Estimating plume cloud height is essential for various applications, such as global climate models. Smokestack plume rise is the constant height at which the plume cloud is carried downwind as its momentum dissipates and the plume cloud and the ambient temperatures equalize. Although different parameterizations are used in most air-quality models to predict the plume rise, they have been unable to estimate it properly. This paper proposes a novel framework to monitor smokestack plume clouds and make long-term, real-time measurements of the plume rise. For this purpose, a three-stage framework is developed based on Deep Convolutional Neural Networks (DCNNs). In the first stage, an improved Mask R-CNN, called Deep Plume Rise Network (DPRNet), is applied to recognize the plume cloud. Then, image processing analysis and least squares theory are respectively used to detect the plume cloud’s boundaries and fit an asymptotic model into their centerlines. The y-component coordinate of this model’s critical point is considered the plume rise. In the last stage, a geometric transformation phase converts image measurements into real-life ones. A wide range of images with different atmospheric conditions, including day, night, and cloudy/foggy, have been selected for the DPRNet training algorithm. Obtained results show that the proposed method outperforms widely-used networks in smoke border detection and recognition.

The time interval set approach depends on results for radiation propagation from star sources, ie spherically symmetric, where properties relate to propagation sphere surfaces, finite for any realistic event and measurement. In contrast the usual vector and one moment time set approach from Newton’s laws, applies to stationary parallel radiation. A time interval only description necessarily has to start from it's own definitions, and properties for time intervals have to be defined with time intervals. This paper is devoted to deriving time interval only set properties commutation, addition and multiplication and derivative, and to define how these can correspond consistently to one moment time set properties. Time development within the time interval description equilibrium depends on the well-known ‘mean velocity theorem’ (MVT) and the time interval version of the Legendre transform, defined in earlier papers. Radiation propagation ‘away from’ and gravitation ‘towards’ the star source can be part of the description. In the discussion parts these properties are applied within astrophysics. Included is a integral derivation of star source radiation energy for both zero and non zero wave particle mass. The time interval set is not one of the usual sets encountered. However it’s non-linearity or non-commutativity property can be related to Noether charges and structure constants, that equal the radiation wave group-velocity, for photons the light velocity c, and, different from the usual derivative to one moment time, to specifics of the time interval only set derivative to time intervals.

Whether it’s cellphones, personal computers, or gaming consoles, technology is a part of everyone’s everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services—AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services’ shared features and unique aspects that they provide to consumers.

It is shown by numerical modelling that anisotropic diffusion can compensate and even overcome diffusion broadening - the main limiting factor of devices working at atmospheric pressure. The thickness of ion clouds coming out of the ionisation chamber is reduced ~ 1000 times in the ADMA inlet increasing the resolution. This finding has a great potential that enables construction of a device with resolution close to that of currently available desktop ion separation devices – high pressure mass spectrometers, but with much lower cost of manufacture and with smaller dimensions. Comprehensive numerical modelling along with mathematical analysis of physical concepts enables reduction of the footprint and manufacturing costs. Modelling determined that the resolving power (RP) potential of the ADMA is greater than the RP of currently available on the market devices for the airport security market. The ADMA technology enables very fast detection of threats; at least 100 times faster than currently used in Ion Mobility Spectrometers (IMS). This potentially allows an increase in passenger throughput in airports.

In recent years, Cloud Computing and Big Data have been considered the most attractive areas that are revolutionizing the IT world. Cloud Computing paradigm has recently appeared that allows running proprietary or difficult portable applications outside their original software environment on one or more virtual hardware platforms. Therefore, we are to developing such techniques which make it possible to secure communication between the communicating Cloud entities. These techniques must take into account several factors due to the data transmitted in this type of environment is proprietary and of significant size. Conventional data security techniques are not suitable for today's cloud usage. Hence, the main research of this thesis is to define an adaptable architecture with the aim to propose a scalable system that supports cloud services. We will define feasible security solutions dedicated to the Cloud computing context in order to robustly protect data stored in the Cloud. We are more precisely looking for working on NoSQL databases. We also intend to propose a secure solution based on the blockchain that has powerful features like decentralization, autonomy, security, reliability, and transparency.

Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

The Median American Sign Language Interpretation Software (ASL) Interpretation Software is a web application that is capable of interpreting American Sign Language in real-time, utilizing an internet connection and a primary web camera, complete with basic phrases and letters. Extensive use of Deep Learning and Neural Networks, specifically Convoluted Neural Networks, enables Median to interpret video inputs and generate accurate results directly displayed to the user in text format. The ultimate goal for Median is to have it act as a bridge between hearing people and members of the deaf community, allowing deaf people to communicate with non-signing people using American Sign Language. Furthermore, Median has been designed to benefit people who lack access to a human ASL Translator, as its format as a website allows it to be accessible anywhere at any time, giving increased availability over human interpreters. Median is designed to be a very versatile program with great potential for growth and expansion.

The Hunga Tonga-Hunga Ha’apai underwater volcano (20.57°S, 175.38°W) violently erupted on 15 January 2022. The volcanic plume evolution during its initial stages is delineated by using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) measurements. The bending angle (BA) anomaly over the Tonga volcanic plume (within 200 km from the eruption center) at 5:17 UTC on 15 January shows a prominent peak at higher stratospheric heights. The top of the BA anomaly revealed that negative to positive change occurred at ~38 km indicating the first height where the RO line-of-sight encounters the volcanic plume. The BA anomaly further revealed an increase of ~50% at ~36 km and confirms the volcanic plume reached above ~36 km. Further, the evolution of BA perturbations within 24 hours after the initial explosion is also discussed. From collocated RO profiles with the volcanic plume, we find a clear descending of the peak altitude of the BA perturbation from ~36 km to ~30 km within 24 hours after the initial eruption. The results from the study will provide some insights into advancing our understanding of volcanic cloud dynamics and their implementation in volcanic plume modeling.

We present the update of the Snow and Ice (SICE) property retrieval algorithm proposed initially by Kokhanovsky et al. (2019). The algorithm is based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The algorithm is available as python and Fortran packages at https://github.com/GEUS-SICE/pySICE. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

The change of tire groove depth will have a huge impact on tire performance, and the use of excessively worn tires is not conducive to the driving safety of automobiles. Tire groove depth detection has become one of the annual inspection items of automobiles, but the research on its related detection technology is still relatively backward. Based on the principle of monocular vision ranging (MVR), image processing technology and cloud platform technology, this paper develops a tire groove depth detection system, which realizes non-destructive detection of tire groove depth. In addition, the system uses the cloud platform to store the test results, and builds a multi-level data management system, allowing car owners to keep track of the tire wear status and historical changes, which is of great significance to ensuring driving safety.

Connected and Autonomous Vehicle (CAV) combines technologies of autonomous vehicle (AV) and connected vehicles (CV) to develop quicker, more reliable and safer traffic. Artificial Intelligence (AI) based CAV solutions play significant roles in sustainable city. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on driver; increased connectivity increases the exposures of vehicles vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles vulnerabilities to negatively impact the performance and operations of CAV. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAV, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAV is elaborated from both viewpoints of intra-vehicle system and inter-vehicle system. We pointed out that securing the perception and operations of CAV would be the top requirement to enable CAV to be applied safely and reliably in practice. Also, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAV.

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

Spatial approach based on the deformation measurement of volcanic dome and crater rim is key to evaluate the activity of a volcano, such as Merapi volcano where associated disaster risk is regularly taking lives. Within this framework, this study aime to detect localized deformation and change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset of 2012 and one UAV-dataset of 2014. The results show that during the period 2012-2014, the crater walls are 100 m to 120 m high above the crater floor at its maximum (North to East-South-East sector), while the West and North sector presents a topographic range of 40 to 80 m. During the period 2012 – 2014, the evolution of the crater rim around the dome is generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 E+04 m3 corresponding to a maximum -9 m (+/- 0.9 m) vertically. This concludes that during the period 2012 – 2014 when the dome of Merapi experienced phreatic or phreatomagmatic explosions, the topography around the dome rose. This rise does not seem to be related to large wall collapses, and it is likely that modification in the subsurface have triggered those changes.

Background: Cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state. Today, observability is often understood as a triad of collecting and processing metrics, distributed tracing data, and logging. The result is often a complex observability system composed of three stovepipes whose data is difficult to correlate. Objective: This study analyzes whether these three historically emerged observability stovepipes of logs, metrics and distributed traces could be handled more integrated and with a more straightforward instrumentation approach. Method: This study applied an action research methodology used mainly in industry-academia collaboration and common in software engineering. The research design utilized iterative action research cycles, including one long-term use case. Results: This study presents a unified logging library for Python and a unified logging architecture that uses the structured logging approach. The evaluation shows that several thousand events per minute are easily processable. Conclusion: The results indicate that a unification of the current observability triad is possible without the necessity to develop utterly new toolchains.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined dis-tance from such surface. We present a new filter, Multi-view and shift rasterization algorithm (MVSR) is based on a different principle, i.e., on the identification of just the lowest points in in-dividual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably. The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). The MVSR filter performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

The popularity of cloud computing is growing owing to its large data storage capacity and high computation power. It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features. Users get benefitted with the selection of the appropriate cloud computing platform for their projects. Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers. This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, universal filters for accurate identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. In this paper, we present a new filter. This Multi-view and shift rasterization algorithm (MVSR) is based on an entirely different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid – after each of these steps, one lowest point per cell is detected. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably (although the profiles were somewhat thicker and not as complete as MVSR-acquired ground). The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). ATIN again performed closest to MVSR, with RMSDs of ground filtered points found above MVSR-based surface at individual sites ranging between 4.5-7.4 cm. The remaining filters performed comparable in the simplest flat area but poorly in rugged and much-vegetated sites, with RMSDs above the MVSR surface ranging at such sites from 21 to 95 cm. In conclusion, the novel filter presented in this paper performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

Background: Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). Methods: Using Design Science Research, this paper introduces a new method, called Cloud Pattern API – Process Mining (CPA-PM), that enables discovering and analyzing cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface (API), with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Results: Applying CPA-PM to a case with real and evolving event logs related to the trial process of a Software-as-a-Service cloud application led to useful analyses and insights, with reusable scripts. Conclusion: CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

We are living in a world where people always strive for the luxuries they can get with less hard work and for these smart systems are developed and established all over the world to make life easier for the people. However, in Pakistan being a third-world country we are unable to achieve better results due to the lack of proper transit from one area to another. Sometimes the roads are not in their optimum conditions and hence people have to face a lot of problems while traveling. In urban areas, people cannot reach their destination in time, in some cases, they damage their vehicles due to the cracks on the roads, in medical emergency patient dies most of the time in their transit from rescuing point to the hospital. Similarly, in rural areas where farmers face countless problems while bringing their yield of the season towards the markets. Therefore, having described the severe damage the bad quality of roads is making. A solution is proposed to solve the problems of both the rural and urban population in our project. The project aims to provide the solution to their problem to a certain extent by monitoring the conditions of the roads. The sensors in the system will calculate the values and send them to the cloud server. The cloud server used is the blynk platform where the data will be stored. Moreover, data can be provided to the government in the future for the timely maintenance of the roads, and hence the citizens and lifestyle of the people will be changed. It is expected that the problems of both urban and rural population will be solved to a greater percentage.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.

Nowadays, information security is a challenge especially when transmitted or shared in public clouds. Many of researchers have been proposed technique which fails to provide data integrity, security, authentication and another issue related to sensitivity data. The most common techniques were used to protect data during transmission on public cloud are cryptography, steganography, and compression. The proposed scheme suggests an entirely new approach for data security on public cloud. Authors have suggested an entirely new approach that completely makes secret data invisible behind carrier object and it is not been detected with the image performance parameters like PSNR, MSE, entropy and others. The details of results are explain in result section of paper. Proposed technique have better outcome than any other existing technique as a security mechanism on a public cloud. Primary focus of suggested approach is to minimize integrity loss of public storage data due to unrestricted access rights by uses. To improve reusability of carrier even after data concealed is really a challenging task and achieved through suggested approach.

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality calling for sustainable measures to reduce the overall consumption and peak load. The dawn of the smart grid concept, embedded systems and ICTs have paved the way to novel HEMS design. In this regard, the Internet of Things (IoT), an enabler for smart and efficient energy management systems is seeing increasing attention for optimizing HEMS design and mitigate its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low-cost, technology availability and scalability. We focus on the backend complexities of IoT Home Area Networks (HAN) using the OCF IoTivity-Lite middleware. To augment the quality, servicing and reduce cost and complexities, this work leverages open-source Cloud technologies from Back4App as BaaS to provide consumer and Utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) and appliance operation control via a HEM App via an Android smartphone.

The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student and staff respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

An entropic origin of gravity is re-visited. Isothermal self-gravitating cloud seen as an ideal gas is analyzed. Gravitational attraction within the isothermal cloud in equilibrium is balanced by the pressure, which is of a pure entropic nature. The notion of the Jeans entropy of the cloud corresponding to the entropy of the self-gravitating cloud in mechanical and thermal equilibrium is introduced. Balance of the gravitational compression and the entropic repulsion yields the scaling relation hinting to the entropic origin of the gravitational force. The analysis of the Jeans instability enables elimination of the “holographic screen” or “holographic principle” necessary for grounding of the entropic origin of gravity.

Here we studied Bianchi type-III string cosmological models generated by means of a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional space-time. To obtain the exact solutions of field equations we consider that the shear scalar and the scalar expansion are proportional, σ∞θ which leads to D=cn and secondly we adopt the assumption considering the Reddy String Condition, ρ+λ=0. From the two different cases obtained here, first case leads to the Bianchi type-III string cosmological model in Lyra geometry in five-dimensional space-time and the second case leads to the five-dimensional vacuum universe in general relativity. The Geometrical and physical properties of the model universe are studied comparing with the present day’s observational findings. The model universe obtained here starts with the big bang and as time progresses both particle density ρp and energy density ρ decreases with expansion of our Universe.

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between Cloud Clients (CC) and Cloud Service Providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a Third-Party Auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state-of-art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance have been discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models have been comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

Molecular Dynamics (MD) simulations model motion of molecules in atomistic detail and aid in drug design. While simulations on large systems may require several days to complete, analysis of terabytes of data generated in the process could also be time consuming. Recent studies captured exciting and dramatic drug-receptor interactions under cell-like complex conditions. Such advances make simulations of biomolecular interactions more realistic, insightful, and informative and have potential to make drug design more realistic. However, currently available resources and techniques do not provide, in reasonable time, a comprehensive understanding of events seen in simulations. We demonstrate that big data approach results in significant speedups, and provides rapid insights into simulations performed. Advancing this improvement, we propose a scalable, self-tuning, and responsive framework based on Cloud-infrastructure to accomplish the best possible MD studies with given priorities and within available resources.

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS got momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is still required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future development by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly while at the same time enthuse them to focus their research inclination in the domain of WBMS.

Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation at the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species.Menhinick’s and Simpson biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR derived canopy height, effective leaf area index data derived from multi-spectral imagery, and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that, 1.) canopy surface and strata variability may be associated with understory species diversity due to habitat partitioning and radiation attenuation, and that, 2.) such a model can predict both this relationship and biodiversity clustering.The study data yielded significant correlations between predictor and response variables and was used to produce a multiple-linear model comprising canopy relief, texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.

Continuous and emerging advances in Information and Communication Technology (ICT) have enabled IoT-to-Cloud applications to be induced by data pipelines coupled with Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet-of-Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or weaker authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected Edge-to Cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication are proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on Dolev-Yao adversarial model while satisfying aspects of the CIA triad.

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤ 100 kg m^-2) and low values of cloud liquid water path (LWP 100–150 g m^-2), the scattering of light snowfall (intensities ≤ 0.5 mm h⁻¹) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m^-2 and the LWP is greater than 100–150 g m^-2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m^-2 and LWP is lower than 100–150 g m^-2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.

With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, often referred to as containers. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability and fault tolerance. Occasionally discovered container escape vulnerabilities allow adversaries to execute code on the host operating system and operate within the cloud infrastructure. We show that docker swarm is currently not secured against misbehaving manager nodes and allows a high impact, high probability privilege escalation attack that we refer to as leadership hijacking. Cloud lateral movement and defense evasion payloads allow an adversary to leverage the docker swarm functionality to control each and every host in the underlying cluster. We demonstrate an end-to-end attack, in which an adversary with access to an application running on the cluster achieves full control of the cluster. To reduce the probability of a successful high impact attack, container orchestration infrastructures must reduce the trust level of participating nodes and in particular, incorporate adversary immune leader election algorithms.

In this ‘New Normal' post-COVID-19 modern world, people's health is becoming increasingly important to track. An advanced technology that uses sensory instruments to track and record critical parameters and communicates with others is the need of the time. It's difficult to keep track of all of the medical parameters and post-operative data of people with non-communicable diseases like diabetes and heart disease on a continuous basis. The system discussed here to be designed for patients who are confined to their homes, particularly when going out and being exposed to the outer world is prohibited. This paper proposed a groundbreaking health management system based on the Internet of Things (IoT) for accessing patient medical parameters in both local and remote areas. When a person's wellbeing becomes urgent, this initiative seeks to send an emergency alert to family members or loved ones. A cloud server records data from the patient's temperature sensor and pulse sensor; the data is analyzed using support vector machine algorithms to identify irregular conditions, and an emergency message is sent to the rest of the family via a mobile application, as well as a warning message to the nearest hospital.

The transition from Building Information Modelling (BIM) to Heritage Building Information Modelling (H-BIM) is intended to pursue an adequate knowledge of the artefact that is to be preserved, progressively replacing the traditional methods of the restoration and structural reinforcement projects with new tools for managing both existing information and new interventions. The aim of the paper is to show the application of the H-BIM method to a stone pavement road located in the Archaeological Site of Pompeii. In detail, starting from a Laser Scanner based survey, juxtaposed by coordinated points georeferenced through a total station, points clouds were handled by means of several BIM-based tools to perform the road design process, starting from the digital elevation model (DEM) to the corridor representation. Subsequently, a visual programming application based on Python language was adopted to update corridor information by means of the object property set. As preliminary results, a tool, complete with graphical and non-graphical information, is proposed to be used in conservation, maintenance and restoration project.

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

Cloud computing nowadays is not an emerging topic, and virtualization is an indispensable technology to expedite cloud computing to become the next sign of the coming Internet revolution. In real life, scientists never stop at exploring the possibilities from such technology by investigating millions of experiments and applications to enhance the quality of virtual services. However, isolated construction for the virtual machine doesn’t save the technology from unwanted data volumes or insensitive processing time. Containers are created to address such problems, by distributing applications without initiating the entire virtual machine. Docker, as an important player in this game, is an open-source application of the container family. The management tool from Docker containers, Swamskit, does not take heterogeneities in either virtualized containers or physical nodes. There are different nodes in the cluster, and each node is different in configurations, resource availability, or concerning resource, etc. Furthermore, the requirements initiated by different services change all the time. The demand might be CPU-intensive (e.g. Clustering services) and also memory-intensive (e.g. Web services), or completely at the opposite. In this paper, we focus on exploring the Docker container cluster and designing, DRAPS, a resource-aware placement scheme, to improve the system performance in a heterogeneous cluster.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

Information has been and will be a vital element for a person or department groups in an organization. That is why there are technologies that help us to give them the proper management of data; Business Intelligence is responsible for bringing technological solutions that correctly and effectively manage the entire volume of necessary and important information for companies. Among the solutions offered by Business Intelligence are Data Warehouses, Data Mining, among other business technologies that working together achieve the objectives proposed by an organization. It is important to highlight that these business technologies have been present since the 50's and have been evolving through time, improving processes, infrastructure, methodologies and implementing new technologies, which have helped to correct past mistakes based on information management for companies. There are questions about Business Intelligence. Could it be that in the not-too-distant future it will be used as an essential standard or norm in any organization for data management, since it provides many benefits and avoids failures at the time of classifying information. On the other hand, Cloud storage has been the best alternative to safeguard information and not depend on physical storage media, which are not 100% secure and are exposed to partial or total loss of information, by presenting hardware failures or security failures due to mishandling that can be given to such information.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral albedo using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi - zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral albedo for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

TThe XPRIZE Foundation designs and operates multi-million-dollar, global competitions to incentivize the development of technological breakthroughs that accelerate humanity toward a better future. To combat the COVID-19 pandemic, the Foundation coordinated with several organizations to make available data sets about different facets of the disease and to provide the computational resources needed to analyze those data sets. This is paper is a case study of the requirements, design, and implementation of the XPRIZE Data Collaborative, a cloud-based infrastructure that enables the XPRIZE to meet its COVID-19 mission and host future data-centric competitions. We examine how a Cloud Native Application can use an unexpected variety of Cloud technologies, ranging from containers, serverless computing, to even older ones like Virtual Machines. We also search and document the effects that the pandemic had on application development in the Cloud. We include our experiences of having users successfully exercise the Data Collaborative, detailing the challenges encountered and areas for improvement and future work.

This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

A micro Sagnac interferometer integration is proposed for electron cloud distributed sensors. The Sagnac interferometer consists of four microring probes integrated into a Sagnac loop. Each of the microring probes is embedded with the silver bars to form the plasmonic wave oscillation. At the center microrings, electrons are trapped and oscillated by the whispering gallery modes (WGMs), where the plasmonic antennas are established and applied for wireless fidelity (WiFi) and light fidelity (LiFi) transmissions for distributed sensors. The antenna gains are 2.59dB, 0.93dB, 1.75dB, and 1.16dB respectively for the four antennas formed at the center microrings. The polarized light of 1.50µm wavelength is fed into the interferometer input, which is polarized randomly into upstream and downstream directions. The polarization components can be obtained by the space-time modulation control. By controlling the electron cloud spin orientation, the space-time projection can be applied, and the ultra-high measurement resolution can be obtained in terms of fast switching time (change in phase). In manipulation, the applied stimuli are substituted by the change in input source power. The light input power variation causes a change in electron cloud density. Similarly, when the electron cloud is excited by the microscopic medium, which can be employed as the microscopic sensors. The WGM sensors have sensitivities of 1.35µm-2, 0.90µm-2, 0.97µm-2 and, 0.81µm-2, respectively. The WGMs behave as a four-point probe for the electron cloud distributed sensors, where the electron cloud sensitivities of 2.31 prads-1mm3 (electrons)-1, 2.27prads-1mm3 (electrons)-1, 2.22 prads-1mm3(electrons)-1, 2.38prads-1mm3(electrons)-1 are respectively obtained.

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate is the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as Building Information Modelling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the afore-mentioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data.

One of the key components of application performance monitoring (APM) software is 2 AI/ML empowered data analytics for predictions, anomaly detection, event correlations and root 3 cause analysis. Time series metrics, logs and traces are three pillars of observability and the valuable 4 source of information for IT operations. Accurate, scalable and robust time series forecasting and 5 anomaly detection are desirable capabilities of the analytics. Approaches based on neural networks 6 (NN) and deep learning gain increasing popularity due to their flexibility and ability to tackle complex 7 non-linear problems. However, some of the disadvantages of NN-based models for distributed cloud 8 applications mitigate expectations and require specific approaches. We demonstrate how NN-models 9 pretrained on a global time series database can be applied to customer specific data using transfer 10 learning. In general, NN-models adequately operate only on stationary time series. Application 11 to non-stationary time series requires multilayer data processing including hypothesis testing for 12 data categorization, category specific transformations into stationary data, forecasting and backward 13 transformations. We present the mathematical background of this approach and discuss experimental 14 results from the productized implementation in Wavefront by VMware (an APM software) while 15 monitoring real customer cloud environments.

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

Despite recent developments in monitoring and visualizing construction progress, the data exchange between construction jobsite and office lacks automation and real-time recording. To address this issue, a near real-time construction work inspection system called iVR is proposed; this system integrates 3D scanning, extended reality, and visual programming to visualize interactive onsite inspection for indoor activities and provide numeric data. iVR comprises five modules: iVR-location finder (finding laser scanner located in the construction site) iVR-scan (capture point cloud data of jobsite indoor activity), iVR-prepare (processes and convert 3D scan data into a 3D model), iVR-inspect (conduct immersive visual reality inspection in construction office), and iVR-feedback (visualize inspection feedback from jobsite using augmented reality). An experimental lab test is conducted to validate the applicability of iVR process; it successfully exchanges required information between construction jobsite and office in a specific time. This system is expected to assist Engineers and workers in quality assessment, progress assessments, and decision-making which can realize a productive and practical communication platform, unlike conventional monitoring or data capturing, processing, and storage methods, which involve storage, compatibility, and time-consumption issues.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operation team. It is followed to integrate the various stages of the development cycle. DevOps is an extended version of the existing Agile method. DevOps aims at continuous integration, Continuous Delivery, Continuous Improvement, faster feedback and security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps

Industria 4.0 is present in smart and digital manufacturing, making manufacturing companies improve productivity, reducing delivery time and related costs. The objective of this work is to demonstrate through three integrated MPS Festo stations (Distribution, Pick \& Place and Sorting), using the Internet of Things and Google Analytics technologies, the benefits in relation to remote performance monitoring. The intended objective is achieved through the implementation of the monitoring system at the three MPS Festo stations. The data obtained through the integration of the Festo stations and their respective sensors are processed and analyzed in a cloud infrastructure, so that the main metrics are visualized and transmitted on a panel. This monitoring system improves the perception of process performance, as the main performance metrics are displayed, such as productivity, cycle time and parts produced. The cloud infrastructure allows remote viewing and monitoring of the system.

Total cloud cover (TCC) retrieval from ground-based optical imagery is a problem being tackled by a few generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there is not very much progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose the framework for the assessment of the algorithms characteristics. We present several new algorithms that are based on deep learning techniques: a model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million of all-sky optical images of visible sky dome taken in various regions of the World Ocean. The research campaigns contributed to DASIO collection took place in the Atlantic ocean, the Indian ocean the Red and Mediterranean seas, and also in the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and validation subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver superior quality compared to all previously published schemes. As a key result, we demonstrate considerable drop of the ability to generalize the training data in case of strong covariate shift between training and validation subsets of imagery which may take place in case of region-aware subsampling.

The explosion of data has transformed the world since much more information is available for collection and analysis than ever before. To extract valuable information from the data in different dimensions, various deep learning models have been developed in the past years. Although these models have demonstrated their strong capability on improving products and services in various applications, training them is still a time-consuming and resource-intensive process. Presently, cloud, one of the most powerful computing infrastructures, has been used for the training. However, how to manage cloud computing resources and to perform the training efficiently is still challenging current techniques. For example, general resource scheduling approaches, such as spread priority and balanced resource schedulers, actually do not work well with deep learning workloads. Besides, the resource allocation problem on a cluster can be divide into two subproblems: (1) local resource optimization: improve resource configuration for a single machine; (2) global resource optimization: improve the cluster-wide resource allocation. In this thesis, we propose two novel container schedulers, FlowCon and SpeCon, that are designed to address these two subproblems respectively and specifically to optimize performance of short-lived deep learning applications in the cloud. FlowCon focuses on resource configuration of single-node in a cluster, as show that it efficiently improves deep learning tasks completion time and resource utilization, and reduces the completion time of a specific job by up to 42.06\% without sacrificing the overall system time. SpeCon targets on cluster-wide resource configuration that speculatively migrate slow-growing models to release resources for fast-growing ones. Based on our experiments, SpeCon improves makespan for up to 24.7\%, compared to current approaches.

The advent of deep learning has completely reshaped our world. Now, our daily life is fulfilled with many well-known applications that adopt deep learning techniques, such as self-driving cars and face recognition. Furthermore, robotics developed more forms of technology which share the same principle with face recognition, such as hand pose recognition and fingerprint recognition. Image recognition technology requires a huge database and various learning algorithms, such as convolutional neural network and recurrent neural network, that requires lots of computational power, such as CPUs and GPUs. Thus, clients could not be satisfied with the computational resource of the local machine. The cloud resource platform emerged at a historic moment. Docker containers play a significant role of microservices-based applications in the next generation. However, it could not guarantee the quality of service. From clients’ perspective, they have to balance the budget and quality of experiences (e.g. response time). The budget leans on individual business owners and the required Quality of Experience (QoE) depends on usage scenarios of different applications, for instance, an autonomous vehicle requires real-time response, but, unlocking your smartphone can tolerate delays. Plenty of on-going projects developed user-oriented optimization resource allocation to improve the quality of the service. Considering the users’ specifications, including accelerating the training process and specifying the quality of experience, this thesis proposes two differentiate containers scheduling for deep learning applications: TRADL and DQoES .

Currently, there are several challenges that Cloud-based health-care Systems, around the world, are facing. The most important issue is to ensure security and privacy or in other words to ensure the confidentiality, integrity and availability of the data. Although the main provisions for data security and privacy were present in the former legal framework for the protection of personal data, the General Data Protection Regulation (GDPR) introduces new concepts and new requirements. In this paper, we present the main changes and the key challenges of the General Data Protection Regulation, and also at the same time we present how the Cloud-based Security Policy methodology proposed in [1] could be modified in order to be compliant with the GDPR and how Cloud environments can assist developers to build secure and GDPR compliant Cloud-based health Systems. The major concept of this paper is, primarily, to facilitate Cloud Providers in comprehending the framework of the new General Data Protection Regulation and secondly, to identify security measures and security policy rules for the protection of sensitive data in a Cloud-based Health System, following our risk-based Security Policy Methodology that assesses the associated security risks and takes into account different requirements from patients, hospitals, and various other professional and organizational actors.

The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMIC) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities – from individual neighborhoods to global regions – that can coordinate local community knowledge with political agency, technical capability, and further research.

The Smartphone industry has expanded significantly over the last few years. According to the available data, each year, a marked increase in the number of devices in use is observed. Most consumers opt for Smartphones due to the extensive number of software applications that can be downloaded on their devices, thus increasing their functionality. However, this growing trend of application installation brings an issue of user protection, as most applications seek permission to access data on a user’s device. The risks this poses to sensitive data is real to both corporate and individual users. While Android has grown in popularity, this trend has not been followed by the efforts to increase the security of its users. This is a well-known set of problems, and prior solutions have approached it from the ground up; that is, they have focused on implementing reasonable security policies within the Android’s open-source kernel. While these solutions have achieved the goals of improving Android with such security policies, they are severely hampered by the way in which they have implemented them. In this work, a framework referred to as CenterYou is proposed to overcome these issues. It applies a pseudo data technique and a cloud-based decision-making system to scan and protect Smartphone devices from unnecessarily requested permissions by installed applications and identifies potential privacy leakages. The current paper demonstrated all aspects of the CenterYou application technical design. The work presented here provides a significant contribution to the field, as the technique based on pseudo data is used in the actual permissions administration of Android applications. Moreover, this system is user and cloud-driven, rather than being governed by over-privileged applications.

While Twitter has been touted to provide up-to-date information about hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision API) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/street networks. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or locations/venue names. This research will help identify reliable crowdsourced risk information to enable near-real time emergency response through better use of crowdsourced risk communication platforms.

Debris-covered glaciers are a notable feature in the greater Himalaya, and their ongoing mass loss under changing climate will affect the water resources of over a billion people. The current knowledge of the mass balance of Himalayan glaciers is restricted by the paucity of in-situ measurements of glaciers in both space and time, as well as the resolution of satellite remote sensing imageries. Recently, the use of Unmanned Aerial System (UAS) imagery has shown the potential to bridge this gap by enabling very detailed monitoring of inaccessible glacial areas. UAS imagery-based monitoring of Himalayan glaciers has so far been limited to a single glacier in the entire Himalaya, providing a limited understanding of spatial variability in glacier mass balance and driving factors. In the first UAS based glacial mass change estimation in the trans-Himalaya, we conducted two Unmanned Aerial System (UAS) surveys (May and November 2019) over the debris-covered Annapurna III glacier in the Himalaya. We performed Structure-from-Motion (SfM) analysis and utilized differential GPS field observations to derive geometrically accurate point clouds, ortho-mosaics and digital surface models (DSMs). The glacial volumetric loss was estimated from DSM differencing, and the magnitude and spatial variability of glacier surface change was derived from 3-D differencing of point clouds. Results revealed a heterogeneous glacial melt pattern, with an average elevation loss of 0.89 m during the monitored time period. The majority of the glacial tongue exhibited surface lowering except the area above and around the glacial snout that surprisingly exhibited significant elevation gain. Both the highest magnitude of mass loss and the highest spatial variability in mass change was observed in areas with exposed ice-cliffs and supraglacial ponds. Glacial surface velocity derived from manual feature tracking showed velocity ranging from 0-4.1 m. A detailed evaluation of specific areas allowed an improved understanding of the complex interplay of factors leading to observed surface change. Our findings expand the extent of UAS based monitoring of debris-covered glaciers in the Himalaya and conclude that UAS derived 3D topographic products will become increasingly important for monitoring of thinning debris-covered glaciers.

Smartphone has become one of the most revolutionary devices in the history of computing. With various kinds of applications, the scope of smartphone has been significantly broadened in the past few years including almost every aspect in our daily life. However, due to the limited on-board resources such as CPU, storage, network bandwidth and battery power, smartphones and the mobile network serving them bring new challenges that have not been encountered in the traditional computing and networking environments. This dissertation focuses on the research areas of improving the network architecture and enhancing the current applications on smartphones. It mainly investigates the areas in the following two directions for three representative categories of mobile services. - In the first direction, the dissertation aims to develop new communication models for smartphone Ad-Hoc networks to achieve efficient communication in the proximity. It is motivated by the fact that smartphone Ad-Hoc networks can help improve the current location-based services and propel new applications. Moreover, the new communication models provide complementary alternatives to the traditional infrastructure-based wireless networks. - In the second direction, the dissertation focuses on improving the other two categories of services, cloud storage services and real-time video streaming services for mobile devices. In the field of cloud storage, we introduce a cloud-assisted approach to provide a set of advanced file operations, such as encryption, decryption and compression, on smartphones. Furthermore, by utilizing the on-board Near Field Communication(NFC) module, we develop an algorithm to securely share the files between mobile devices. For the services of real-time video streaming, we propose approaches that identify a user’s status by analyzing the accelerometer data and then, dynamically adjust the buffer mechanism to save network bandwidth on smartphones.

The landscape of centralized cloud computing is now changing to distributed and decentralized clouds with promising impacts on energy consumption, resource availability, resilience, and customer experience. This research highlights the impacts of emerging IT trends, namely, 5G wireless technology, blockchain, and industrial Artificial Intelligence (AI) in development and realization of the next generation of cloud computing. Integration of these technologies in cyber-physical system and cloud manufacturing paradigms is explained and a unified edge-fog-cloud architecture is proposed for successful implementation in manufacturing systems.

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

Abstract—as the complexity in the cloud services increases day by day the role of brokers used in the cloud gain more importance. Here we basically resolve this issue by discussing preference based cloud service recommender that support MCDM approach.^[24] The implementation and specifications details are properly discussed in a unified way to deal with the problem. SC3 is a tool that compromises capabilities of the quality assurance dimension of CSB and also it strengthen the flexibility of cloud services. The anxieties between definition procedure and implementation procedure are separated by service completeness compliance checker (SC3). Now a days companies use cloud computing for their economic benefits and market competitions that increase the demand of “cloud computing”. So for the calculation of critical success factor of “cloud computing” we focus on plan do check act strategy.

On 25 May 2018, the General Data Protection Regulation (GDPR)Article 17, the Right to Erasure (‘Right to be Forgotten’) came into force making it vital for organisations to identify, locate and delete all Personally Identifiable Information (PII) where a valid request is received from a data subject to erase their PII and the contractual period has expired. This must be done without undue delay and the organisation must be able to demonstrate reasonable measures were taken. Failure to comply may incur significant fines, not to mention impact to reputation. Many organisations do not understand their data, and the complexity of a hybrid cloud infrastructure means they do not have the resources to undertake this task. The variety of available tools are quite often unsuitable as they involve restructuring so there is one centralised data repository. This research aims to demonstrate compliance with GDPR’s Article 17 Right to Erasure (‘Right to be Forgotten’) is achievable in a Hybrid cloud environment by following a list of recommendations. However, 100% retrieval, 100% of time will not be possible, but we show that small organisations running an ad-hoc Hybrid cloud environment can demonstrate that reasonable measures were taken to be Right to Erasure (‘Right to be Forgotten’) compliant.

In this manuscript, an automated framework dedicated to design space exploration and design optimization studies is presented. The framework integrates a set of numerical simulation, computer-aided design, numerical optimization, and data analytics tools using scripting capabilities. The tools used are open-source and freeware, and can be deployed on any platform. The main feature of the proposed methodology is the use of a cloud-based parametrical computer-aided design application, which allows the user to change any parametric variable defined in the solid model. We demonstrate the capabilities and flexibility of the framework using computational fluid dynamics applications; however, the same workflow can be used with any numerical simulation tool (e.g., a structural solver or a spread-sheet) that is able to interact via a command line interface or using scripting languages. We conduct design space exploration and design optimization studies using quantitative and qualitative metrics, and to reduce the high computing times and computational resources intrinsic to these kinds of studies, concurrent simulations and surrogate-based optimization are used.

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

Industry 4.0 is having a great impact in all smart efforts. This is not a single product, but is composed of several technologies, being one of them Industrial Internet of Things (IIoT). Currently, there are very varied implementation options offered by several companies, and this imposes a new challenge to companies that want to implement IoT in their processes. This challenge suggests to use multi-criteria analysis to make a repeatable and justified decision, requiring a set of alternatives and criteria. This paper proposes a new methodology and comprehensive criteria to help organizations to take an educated decision by applying multi-criteria analysis. Here, we suggest a new original use of PROMETHEE-II with full example from weight calculation up to IoT platform selection, showing this methodology as an effective study for other organizations interested to select an IoT platform. The criteria proposed outstands from previous work by including not only technical aspects, but economic and social criteria, providing a full view of the problem analyzed. A case of study was used to prove this proposed methodology.

Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this manuscript we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

As one of GEO-constellation for environmental monitoring in the next decade, Geostationary Environment Monitoring Spectrometer (GEMS) is designed to observe the Asia Pacific region to provide the information on the atmospheric chemicals, aerosol and cloud properties. For the continuous monitoring of the sensor performance after its launch in early 2020, here we suggest deep convective clouds (DCCs) as a possible target for the vicarious calibration of GEMS, the first UV/VIS hyperspectral sensor onboard a geostationary satellite. Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) are used as a proxy of GEMS, and a conventional DCC detection approach applying the thermal threshold test is used for the DCC detection based on the collocations with Advance Himawari-8 Imager (AHI) onboard Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area on average over 200 pixels in a single observation scene. Considering the spatial resolution of GEMS, 3.5 km×7 km which is similar to TROPOMI, and its temporal resolution (8 times a day), availability of DCCs for vicarious calibration of GEMS is expected to be sufficient. Inspection of the DCC reflectivity spectra estimated from the OMI and TROPOMI data also shows a promising result. Even though, their observation geometry and sensor characteristics are quite a different, the estimated DCC spectra agree quite a well within a known uncertainty range with comparable spectral features. When the DCC detection is further improved by applying both visible and infrared tests, the variability of DCC reflectivity from the TROPOMI data is reduced by half, from 10% to 5%. This is mainly due to the efficient screening of cold thin cirrus with the visible test and of bright warm clouds with the infrared test. The precise DCC detection is also expected to contribute to the accurate characterization of the cloud reflectivity, which will be further investigated.

Quality of Service (QoS) has a significant role in the provision of resources within service oriented distributed systems. In Quality of Service, cloud computing creates new challenges for improvements using the concept of virtualization. Currently, Cloud Computing is very emerging technology in every field of data storage and resource distribution over the network. Considering this new emerging technology, for the ease of data accessibility, price, resource use, restoration, response time and number of constraints the quality performance measures need to be upgraded. The paper highlights the research gap in providing a solution to achieve a Quality of Services in Cloud Computing. We also review the issues and challenges arising in cloud computing to guarantee quality.

Mobile Cloud Computing(MCC) is a recent technology used by various users worldwide. In 2015, more than 240 million users used mobile cloud computing which gives a profit of $5.2 billion to service providers. MCC is a combination of Mobile computing and cloud computing. By the combination of these two, it gives various challenges like network access, elasticity, management, availability, security, privacy, etc. Here security issues are considered because both the security issues of mobile computing and cloud computing are considered as important like data security, virtualization security, partitioning security, Mobile cloud application security, Mobile device security. This paper gives a detailed study of security issues in mobile cloud computing and its prevention measures.

The continuous increase in tuition fees in university education in many countries requires justification by the university authorities through improved learning resources that can guarantee employment opportunities for the students through hands-on industry training. This paper describes the design of curriculum of cloud computing module in collaboration with Amazon Web Services (AWS) Academy to include industry-based practical hands-on labs in the curriculum to improve the employability of Internet of Things (IoT) engineering students. This study introduces industry best practices and hands-on labs in cloud computing and discovered that students’ understanding and learning experience in the subject increase when exposed to real-life applications. The study blends academic theories in cloud computing with their applications as obtained in industry to enable students to have both the theoretical and practical skills that will prepare them for careers in cloud computing. To achieve this, cloud computing lecturers were trained by AWS Academy as a prerequisite to ensure that the tutors themselves acquire hands-on proficiency in cloud computing technologies. The study finds that students tend to be more engaged and learn better when academic theories and concepts are combined with real-world applications as obtained in the industry.

Although the Slicing Method (SM) is effective for calculating the volume of point cloud objects (PCOs), it is restricted in terms of applicability and practicability because of a certain contingency and directional defects. The Co-Opposite-Direction Slicing Method (CODSM) proposed in this paper is an improved method for calculating PCO volume by increasing parallel (co-opposite-direction) observation and considering the two-way mean as the result. This method takes full advantage of the mutual offsetting of random errors and the compensation of systematic directional errors, which can effectively overcome (or mitigate) the effect of random errors and reduce the effect of systematic errors in SM. In this paper, two typical objects, a cone model and a stone lion base, are the examples for calculating PCO volume using CODSM. The results show that CODSM has all the inherent advantages of SM and effectively weakens the volatility of random errors and the directionality of systematic errors from SM. Therefore, CODSM is a robust configuration upgrade of SM.

The 21^st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.

Warm cloud-precipitation plays a vital role in the hydrological cycle, weather, and climate. Comprehensive observation and study of warm cloud-precipitation can advance our understanding of the internal physical processes and provide valuable information for developing the numerical models. This paper mainly focused on a study of characteristics of warm cloud-precipitation in South China during the pre-flood season using datasets observed from a Ka-band cloud radar, laser ceilometer and disdrometer. Eighteen kinds of quantities from these three instruments were used to precisely elucidate the distribution, diurnal variation, vertical structure, and physical property of warm cloud-precipitation. The results showed that the occurrence of aloft cloud-precipitation decreased with the increase of height, and most of the hydrometeors were distributed below 2 km. During the observation period, the ground rainfall mainly came from light precipitation; however, short-time and sharp showers contributed to the majority of rain amounts. Most of the cloud layers were single-layer, with base heights below 2.2 km, thickness thinner than 2.1 km, and top heights within 0.6-4.2 km. Warm cloud-precipitation owned certain diurnal variations, with a rising trend of cloud base heights in the afternoon and midnight. During 0230-1100, 1200-1800, and 2100-2300, the convections were relatively active with higher cloud tops, thicker cloud thickness, and higher rainfall occurrences. Separation and statistical results of cloud and precipitation indicated that they owned different vertical structures and physical properties, exhibiting different value ranges and changes of radar reflectivity, vertical air motion, particle size, number concentration, liquid water, and rain rate at different height levels. The particle size distributions of cloud and precipitation both were exponential. Radar-derived raindrop size distribution was very coherent with the ground measurement when the reflectivity of precipitation was within 10-20 dBZ. However, for other reflectivity regimes, instrument sensitivity, sampling height, attenuation, and non-precipitating weak targets can affect the comparison.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

Traditionally, studies on freeze–thaw erosion have used the analytic hierarchy process (AHP) to calculate the weight of evaluation factors, however, this method cannot accurately depict the fuzziness and randomness of the problem. To overcome this disadvantage, the present study has proposed an improved AHP method based on the cloud model to evaluate the impact factors in freeze–thaw erosion. To establish an improved evaluation method for freeze–thaw erosion in Tibet, the following six factors were selected: annual temperature range, average annual precipitation, slope, aspect, vegetation coverage, and topographic relief. The traditional AHP and the cloud model were combined to determine the weight of the impact factors, and a consistency check was performed. The comprehensive evaluation index model was used to evaluate the intensity of freeze–thaw erosion in Tibet. The results show that freeze–thaw erosion is extensive, stretching over approximately 66.1% of Tibet. The problem is the most serious in Ngari Prefecture and Nagqu. However, mild erosion and moderate erosion, accounting for 37.1% and 25.0%, respectively, of the total freeze–thaw erosion are the most widely distributed. The evaluation results for the freeze–thaw erosion was confirmed to be consistent with the actual situation. In brief, this study provided a new approach to evaluate the conditions of freeze–thaw erosion quantitively in Tibet.

Imaging lidars are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a career to decide the final solution to be implemented. The diversity of state-of-the art approaches to the solution brings, however, a large uncertainty towards such decision. This results often in contradictory claims from different manufacturers and developers. Within this paper we intend to provide an introductory overview of the technology linked to imaging lidars for autonomous vehicles. We start with the main single-point measurement principles, and then present the different imaging strategies implemented 8 in the different solutions. An overview of the main components most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development has been included, 10 in order to discuss some of the problems which still need to be solved before an efficient final implementation.Beyond this introduction, the reader is provided with a detailed bibliography containing both relevant books and state of the art papers.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

One challenging problem is the representation of three-dimensional datasets that vary with time. These datasets can be though as a cloud of points that gradually deforms. But point-wise variations lack of information about the overall deformation pattern, and more importantly, about the extreme deformation locations inside the cloud. The present article applies a technique in computational mechanics to derive the strain-rate state of a time-dependent and three-dimensional data distribution, by which one can characterize its main trends of shift. Indeed, the tensorial analysis methodology is able to determine the global deformation rates in the entire dataset. With the use of this technique, one can characterize the significant fluctuations in a reduced multivariate description of an urban system and identify the possible causes of those changes: calculating the strain-rate state of a PCA-based multivariate description of an urban system, we are able to describe the clustering and divergence patterns between the districts of the city and to characterize the temporal rate in which those variations happen.

Cloud-based CNC is an emerging paradigm of Industry 4.0 where computer numerical control (CNC) functionalities are moved to the cloud and provided to manufacturing machines as a service. Among many benefits, C-CNC allows manufacturing machines to leverage advanced control algorithms running on cloud computers to boost their performance at low cost, without need for major hardware upgrades. However, a fundamental challenge of C-CNC is how to guarantee safety and reliability of machine control given variable Internet quality of service, especially on public Internet networks. We propose a three-tier redundant architecture to address this challenge. We then prototype tier one of the architecture on a 3D printer successfully controlled via C-CNC over public Internet connections, and discuss follow-on research opportunities.

The amount and spatial distribution of foliage in a tree canopy have fundamental functions in ecosystems as they affect energy and mass fluxes through photosynthesis and transpiration. They are usually described by the Leaf Area Index (LAI) and the Leaf Area Density (LAD), which can be measured through a variety of methods, including voxel-based methods applied to LiDAR point clouds. A theoretical study recently compared the numerical errors arising from different voxel-based estimation methods for Plant Area Density (PAD) based on Beer’s law-based, contact frequency and Maximum-Likelihood Estimation, showing that the bias-corrected Maximum Likelihood Estimator was theoretically the most efficient. However, this earlier study i) ignored wood volumes; ii) neglected vegetation clumping inside the voxel; iii) ignored instrument characteristics in terms of effective footprint, iv) was limited to a single viewpoint. In practice, retrieving LAD from PAD is not straightforward, vegetation is not randomly distributed in volumes of interest, beams are divergent and forestry plots are usually sampled from more than one viewpoint, to mitigate the effect of occlusion. In the present short communication, we extend the previous efficient formulation to actual field conditions to i) account for the presence of both wood volumes and wood hits, ii) rigorously include correction terms for vegetation and instrument characteristics, iii) integrate multiview data. A numerical comparison with other methods commonly used to combine information from different viewpoints led to error reduction, especially in poorly-explored volumes, which are frequent in actual canopies. Beyond its concision, completeness and efficiency, this new formulation -which can be applied to multiview TLS, but also UAV LiDAR scanning - can help reducing errors in LAD estimation.

A wealth of Full Waveform (FW) LiDAR data are available to the public from different sources, which is poised to boost the extensive application of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, adaptive Gaussian and Weibull decompositions, and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed through converting waveforms into points to form the Hyper Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data, but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales. The package and code examples can be found at https://github.com/tankwin08/waveformlidar.

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

Reliability is essential in industrial networks. In addition, most of the data from nodes of industrial Internet of Things (IIoT) are generated in real time. Thus, those data are mainly used for the time-sensitive applications. Furthermore, device failures should be considered when modeling reliable fog computing for IIoT. In this paper, we provide fundamental aspects to model reliable fog computing for IIoT. First, existing models of fog computing are compared. Then, the most feasible communication type to achieve a reliable system is determined from model analysis. Interaction modes are elaborated to study the advantages and drawbacks when communication is deployed in fog computing for IIoT, and challenges and solutions for reliable fog computing are discussed.