Relevant bibliographies by topics / Computing Methodologies

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computing Methodologies'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Computing Methodologies"

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Ogala, Justin Onyarin, and Ese Sophia Mughele. "Agile Software Development Methodologies in Cloud Computing." Advances in Multidisciplinary and scientific Research Journal Publication 1 (July 30, 2022): 67–80. http://dx.doi.org/10.22624/aims/rebk2022-p7.

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Department of Computer Science Faculty of Computing, University of Delta, Agbor, Nigeria E-mails: justin.ogala@unidel.edu.com; s.mughele@unidel.edu.ng ABSTRACT In the software sector, agile development approaches have a lot of promise. Agile development methodologies are highly realistic in their awareness of the fact that business requirements change all the time. By executing software releases iteratively and collecting user feedback more often, agile development approaches maximize the benefits of cloud computing. This study is a cloud computing and Agile Methods analysis. This article examines Agile management and development approaches, as well as their benefits when used in conjunction with cloud computing. By executing software releases iteratively and obtaining user feedback very often, agile development practices maximize the benefits of cloud computing. The basic proposition of this study is that Small and medium enterprises should use a framework that guides their migration to cloud computing to reduce all the issues that come with cloud computing infrastructures Keywords: Agile Methodologies, Cloud Computing, Software Development, Integration
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Furuhashi, Takeshi. "Soft Computing Methodologies for Intelligent Systems." IEEJ Transactions on Industry Applications 118, no. 3 (1998): 280–83. http://dx.doi.org/10.1541/ieejias.118.280.

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Papadrakakis, Manolis, and Nikos D. Lagaros. "Soft computing methodologies for structural optimization." Applied Soft Computing 3, no. 3 (2003): 283–300. http://dx.doi.org/10.1016/s1568-4946(03)00040-1.

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Sarpudin, Sarpudin, Fajri Rahmadeli, and Hadi Supratikta. "The Impact of Cloud Computing on System Lifecycle Methodologies: an Innovative Approach to Scalability Flexibility." Indonesian Development of Economics and Administration Journal 3, no. 1 (2024): 73–81. http://dx.doi.org/10.70001/idea.v3i1.213.

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This research analyzes the impact of cloud computing on traditional system lifecycle methodologies and offers an innovative approach that leverages the advantages of cloud computing. Qualitative research was conducted with a literature study on conventional system lifecycle methodologies and cloud computing. The results show that cloud computing enables increased scalability and flexibility through a service-based approach, automation, and elastic management of resources. This research provides recommendations for implementing a hybrid lifecycle methodology that leverages the advantages of both cloud and traditional systems.This paper discusses the impact of cloud computing on system lifecycle methodologies. Cloud computing offers an innovative approach to system scalability and flexibility by allowing computing and storage resources to scale dynamically according to demand. Traditional system lifecycle methodologies are becoming less relevant for rapidly changing cloud environments, so they need to be adapted to respond to the scalability and flexibility capabilities offered by cloud computing.This paper evaluates the impact of the shift towards cloud computing on the methodologies used in the system development lifecycle. Cloud computing offers the ability to scale elastically according to demand, which opens up opportunities to adopt a more adaptable and flexible approach to system development. This paper discusses how traditional system lifecycle methodologies can be adapted to accommodate key cloud computing characteristics such as scalability, flexibility, and virtualization of computing resources. This paper reviews the implications of the move towards a cloud computing paradigm on the methodologies used in developing and maintaining software systems. The shift towards cloud-based environments offers the advantages of scalability and flexibility in dynamically managing system resources. However, these key characteristics of cloud computing pose challenges to traditional system lifecycle methodologies designed for on-premise environments. This paper discusses how system lifecycle methodologies can be adapted to better support system development in cloud computing environments.
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Yoshida, Makoto, and Kazumine Kojima. "High Performance Computing Design by Code Migration for Distributed Desktop Computing Grids." International Journal of Grid and High Performance Computing 3, no. 4 (2011): 53–70. http://dx.doi.org/10.4018/jghpc.2011100105.

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Large scale loosely coupled PCs can organize clusters and form desktop computing grids on sharing each processing power; power of PCs, transaction distributions, network scales, network delays, and code migration algorithms characterize the performance of the computing grids. This article describes the design methodologies of workload management in distributed desktop computing grids. Based on the code migration experiments, transfer policy for computation was determined and several simulations for location policies were examined, and the design methodologies for distributed desktop computing grids are derived from the simulation results. The language for distributed desktop computing is designed to accomplish the design methodologies.
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Taylor, M. J., E. P. Moynihan, and A. T. Wood-Harper. "End-user computing and information systems methodologies." Information Systems Journal 8, no. 1 (1998): 85–96. http://dx.doi.org/10.1046/j.1365-2575.1998.00005.x.

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Roibas, Anxo Cereijo. "Teaching appropriate ethnographic methodologies for pervasive computing." International Journal of Teaching and Case Studies 1, no. 1/2 (2007): 135. http://dx.doi.org/10.1504/ijtcs.2007.014215.

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Gilbert Fakeyede, Ololade, Evelyn Chinedu Okeleke, Patrick Azuka Okeleke, and Olubukola Rhoda Adaramodu. "A COMPREHENSIVE REVIEW OF IT AUDIT METHODOLOGIES IN THE AGE OF QUANTUM COMPUTING." JOURNAL OF TECHNOLOGY & INNOVATION 3, no. 2 (2023): 85–92. http://dx.doi.org/10.26480/jtin.02.2023.85.92.

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The emergence of quantum computing presents a profound challenge and opportunity for information technology (IT) audit methodologies and IT security. Quantum computing’s potential to break classical encryption methods and its promise of exponential computational power necessitate a proactive response. This comprehensive review explores the fundamentals of quantum computing, the vulnerabilities of classical encryption, the transition to quantum-safe encryption, and the role of IT auditors in navigating this quantum landscape. Additionally, we address emerging quantum technologies, ethical considerations, and the interplay between quantum computing and IT security. To thrive in the quantum era, organisations are advised to plan for quantum-safe transitions, invest in quantum-resistant cryptography, monitor evolving regulations, develop quantum-ready workforces, integrate Quantum Key Distribution (QKD), and adopt a long-term security strategy. Adapting IT audit methodologies to the quantum era requires a multidisciplinary approach, and the recommendations provided here aim to guide organisations in securing their digital assets in this transformative quantum-empowered future.
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AR, Manu, Dr Vinod Kumar Agrawal, and Dr K. N. Bala Subramanya Murthy. "Representation of cloud ecosystem using engineering methodologies." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (2013): 518–32. http://dx.doi.org/10.24297/ijct.v7i1.3482.

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Cloud Computing has fascinated massive consideration for business in spite of lot of technologies and business models in the market. The operational particulars within the cloud are not coherent enough to customers. Hence, this paper provides the method to derive trusted cloud computing scaffold. This scaffold is represented using the engineering methodologies. In this work we use elementary engineering principles of computation to represent the cloud eco-system, comprising of complete cloud system from the end-user perspective.
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Kumar Ale, Narendar. "Enhancing Business Operations: Importance and Methodologies of Cloud Computing Security Testing." International Journal of Science and Research (IJSR) 9, no. 10 (2020): 1797–99. http://dx.doi.org/10.21275/sr24619055641.

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Dissertations / Theses on the topic "Computing Methodologies"

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Cabrera, Añon Guillem. "Service allocation methodologies for contributory computing environments." Doctoral thesis, Universitat Oberta de Catalunya, 2014. http://hdl.handle.net/10803/295714.

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Aquesta tesi adreça el problema de l'assignació eficient de recursos en sistemes de computació contributius. Aquest tipus de sistemes estan formats únicament per nodes no dedicats i requereixen de mecanismes adequats per a optimitzar l'ús dels recursos disponibles. La tesi proposa algorismes que optimitzen diferents aspectes d'aquest tipus de sistemes, com la disponibilitat de serveis compostos, el consum energètic originat per un servei o la distància entre qualsevol client i una rèplica en una topologia de xarxa coneguda. Aquests algorismes es basen en la combinació de meta-heurístiques i tècniques de simulació, cosa que permet que es puguin adaptar a situacions molt diverses i ser aprofitats pels sistemes contributius.<br>Esta tesis trata el problema de la asignación eficiente de recursos en sistemas de computación contributivos. Este tipo de sistemas estan compuestos de manera exclusiva por nodos no dedicados y requieren de mecanismos orientados a optimizar el uso de los recursos disponibles. La tesis propone algoritmos que optimizan diferentes aspectos de este tipo de sistemas, como la disponibilidad de servicios compuestos, el consumo energético de un servicio o la distancia entre cualquier cliente y una réplica en una topologia de red conocida. Estos algoritmos se basan en la combinación de metaheurísticas y tecnicas de simulación, cosa que permite que puedan adaptar su funcionamiento a situaciones muy diversas i así ser aprovechados por los sistemas contributivos.<br>This thesis addresses the efficient allocation problem in contributory computing systems. These systems are built only from non-dedicated nodes and require proper mechanisms in order to optimize the resource utilization. The thesis proposes algorithms that optimize different aspects of these systems, such as the availability of composite services, the energy consumptions of a service or the network distance between any client and a service replica in a known network topology. The algorithms are based in metaheuristics combines with simulation techniques, which allows them to adapt to very different scenarios and to be harnessed by contributory systems.
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Alomar, Barceló Miquel Lleó. "Methodologies for hardware implementation of reservoir computing systems." Doctoral thesis, Universitat de les Illes Balears, 2017. http://hdl.handle.net/10803/565422.

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[cat]Inspirades en la forma en què el cervell processa la informació, les xarxes neuronals artificials (XNA) es crearen amb l’objectiu de reproduir habilitats humanes en tasques que són difícils de resoldre mitjançant la programació algorítmica clàssica. El paradigma de les XNA s’ha aplicat a nombrosos camps de la ciència i enginyeria gràcies a la seva capacitat d’aprendre dels exemples, l’adaptació, el paral·lelisme i la tolerància a fallades. El reservoir computing (RC), basat en l’ús d’una xarxa neuronal recurrent (XNR) aleatòria com a nucli de processament, és un model de gran abast molt adequat per processar sèries temporals. Les realitzacions en maquinari de les XNA són crucials per aprofitar les propietats paral·leles d’aquests models, les quals afavoreixen una major velocitat i fiabilitat. D’altra banda, les xarxes neuronals en maquinari (XNM) poden oferir avantatges apreciables en termes de consum energètic i cost. Els dispositius compactes de baix cost implementant XNM són útils per donar suport o reemplaçar el programari en aplicacions en temps real, com ara de control, supervisió mèdica, robòtica i xarxes de sensors. No obstant això, la realització en maquinari de XNA amb un nombre elevat de neurones, com al cas de l’RC, és una tasca difícil a causa de la gran quantitat de recursos exigits per les operacions involucrades. Tot i els possibles beneficis dels circuits digitals en maquinari per realitzar un processament neuronal basat en RC, la majoria d’implementacions es realitzen en programari usant processadors convencionals. En aquesta tesi, proposo i analitzo diverses metodologies per a la implementació digital de sistemes RC fent ús d’un nombre limitat de recursos de maquinari. Els dissenys de la xarxa neuronal es descriuen en detall tant per a una implementació convencional com per als distints mètodes alternatius. Es discuteixen els avantatges i inconvenients de les diferents tècniques pel que fa a l’exactitud, velocitat de càlcul i àrea requerida. Finalment, les implementacions proposades s’apliquen a resoldre diferents problemes pràctics d’enginyeria.<br>[spa]Inspiradas en la forma en que el cerebro procesa la información, las redes neuronales artificiales (RNA) se crearon con el objetivo de reproducir habilidades humanas en tareas que son difíciles de resolver utilizando la programación algorítmica clásica. El paradigma de las RNA se ha aplicado a numerosos campos de la ciencia y la ingeniería gracias a su capacidad de aprender de ejemplos, la adaptación, el paralelismo y la tolerancia a fallas. El reservoir computing (RC), basado en el uso de una red neuronal recurrente (RNR) aleatoria como núcleo de procesamiento, es un modelo de gran alcance muy adecuado para procesar series temporales. Las realizaciones en hardware de las RNA son cruciales para aprovechar las propiedades paralelas de estos modelos, las cuales favorecen una mayor velocidad y fiabilidad. Por otro lado, las redes neuronales en hardware (RNH) pueden ofrecer ventajas apreciables en términos de consumo energético y coste. Los dispositivos compactos de bajo coste implementando RNH son útiles para apoyar o reemplazar al software en aplicaciones en tiempo real, como el control, monitorización médica, robótica y redes de sensores. Sin embargo, la realización en hardware de RNA con un número elevado de neuronas, como en el caso del RC, es una tarea difícil debido a la gran cantidad de recursos exigidos por las operaciones involucradas. A pesar de los posibles beneficios de los circuitos digitales en hardware para realizar un procesamiento neuronal basado en RC, la mayoría de las implementaciones se realizan en software mediante procesadores convencionales. En esta tesis, propongo y analizo varias metodologías para la implementación digital de sistemas RC utilizando un número limitado de recursos hardware. Los diseños de la red neuronal se describen en detalle tanto para una implementación convencional como para los distintos métodos alternativos. Se discuten las ventajas e inconvenientes de las diversas técnicas con respecto a la precisión, velocidad de cálculo y área requerida. Finalmente, las implementaciones propuestas se aplican a resolver diferentes problemas prácticos de ingeniería.<br>[eng]Inspired by the way the brain processes information, artificial neural networks (ANNs) were created with the aim of reproducing human capabilities in tasks that are hard to solve using the classical algorithmic programming. The ANN paradigma has been applied to numerous fields of science and engineering thanks to its ability to learn from examples, adaptation, parallelism and fault-tolerance. Reservoir computing (RC), based on the use of a random recurrent neural network (RNN) as processing core, is a powerful model that is highly suited to time-series processing. Hardware realizations of ANNs are crucial to exploit the parallel properties of these models, which favor higher speed and reliability. On the other hand, hardware neural networks (HNNs) may offer appreciable advantages in terms of power consumption and cost. Low-cost compact devices implementing HNNs are useful to suport or replace software in real-time applications, such as control, medical monitoring, robotics and sensor networks. However, the hardware realization of ANNs with large neuron counts, such as in RC, is a challenging task due to the large resource requirement of the involved operations. Despite the potential benefits of hardware digital circuits to perform RC-based neural processing, most implementations are realized in software using sequential processors. In this thesis, I propose and analyze several methodologies for the digital implementation of RC systems using limited hardware resources. The neural network design is described in detail for both a conventional implementation and the diverse alternative approaches. The advantages and shortcomings of the various techniques regarding the accuracy, computation speed and required silicon area are discussed. Finally, the proposed approaches are applied to solve different real-life engineering problems.
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Subramaniam, Balaji. "Metrics, Models and Methodologies for Energy-Proportional Computing." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/56492.

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Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Exacerbating such costs, data centers are often over-provisioned to avoid costly outages associated with the potential overloading of electrical circuitry. However, such over provisioning is often unnecessary since a data center rarely operates at its maximum capacity. It is imperative that we realize effective strategies to control the power consumption of the server and improve the energy efficiency of data centers. Adding to the problem is the inability of the servers to exhibit energy proportionality which diminishes the overall energy efficiency of the data center. Therefore in this dissertation, we investigate whether it is possible to achieve energy proportionality at the server- and cluster-level by efficient power and resource provisioning. Towards this end, we provide a thorough analysis of energy proportionality at the server and cluster-level and provide insight into the power saving opportunity and mechanisms to improve energy proportionality. Specifically, we make the following contribution at the server-level using enterprise-class workloads. We analyze the average power consumption of the full system as well as the subsystems and describe the energy proportionality of these components, characterize the instantaneous power profile of enterprise-class workloads using the on-chip energy meters, design a runtime system based on a load prediction model and an optimization framework to set the appropriate power constraints to meet specific performance targets and then present the effects of our runtime system on energy proportionality, average power, performance and instantaneous power consumption of enterprise applications. We then make the following contributions at the cluster-level. Using data serving, web searching and data caching as our representative workloads, we first analyze the component-level power distribution on a cluster. Second, we characterize how these workloads utilize the cluster. Third, we analyze the potential of power provisioning techniques (i.e., active low-power, turbo and idle low-power modes) to improve the energy proportionality. We then describe the ability of active low-power modes to provide trade-offs in power and latency. Finally, we compare and contrast power provisioning and resource provisioning techniques. This thesis sheds light on mechanisms to tune the power provisioned for a system under strict performance targets and opportunities to improve energy proportionality and instantaneous power consumption via efficient power and resource provisioning at the server- and cluster-level.<br>Ph. D.
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Fayyaz, Ahmad. "Energy Efficient Resource Scheduling Methodologies for Cluster and Cloud Computing." Diss., North Dakota State University, 2015. https://hdl.handle.net/10365/27936.

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One of the major challenges in the High Performance Computing (HPC) clusters, Data Centers, and Cloud Computing paradigms is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The key novelty in our work is utilization of the Dynamic Threshold-Voltage Scaling (DTVS) for the reduction of cumulative power utilized by each node in the cluster. Moreover, to enhance the performance of the resource scheduling strategies in first part of the work, independent tasks within a job are scheduled to most suitable Computing Nodes (CNs). First, our research analyzes and compares eight scheduling techniques in terms of energy consumption and makespan. Primarily, the most suitable Dynamic Voltage Scaling (DVS) level adhering to the deadline is identified for each of the CNs by the scheduling heuristics. Afterwards, the DTVS is employed to scale down the static, as well as dynamic power by regulating the supply and bias voltages. Finally, the per node threshold scaling is used attain power saving. Our simulation results affirm that the proposed methodology significantly reduces the energy consumption using the DTVS. The work is further extended and the effect of task consolidation is studied and analyzed. By consolidating the tasks on a fewer number of servers the overall power consumed can be significantly reduced. The tasks are first allocated to suitable servers until all the tasks are exhausted. The idle servers are then turned off by using DTVS. The Virtual Machine (VM) monitor checks for under-utilized, partially filled, over-utilized, and empty servers. The VM monitor then migrates the tasks to suitable servers for execution if a set of conditions is met. By this way, many servers those were under-utilized get free and are turned off by using DTVS to save power. Simulations results confirm our study and a substantial reduction in the overall power consumption of the cloud data center is observed.
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Wilkes, Charles Thomas. "Programming methodologies for resilience and availability." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/8308.

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HANDA, MANISH. "ONLINE PLACEMENT AND SCHEDULING ALGORITHMS AND METHODOLOGIES FOR RECONFIGURABLE COMPUTING SYSTEMS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100030953.

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Pauley, Wayne A. Jr. "An Empirical Study of Privacy Risk Assessment Methodologies in Cloud Computing Environments." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/271.

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Companies offering services on the Internet have led corporations to shift from the high cost of owning and maintaining stand-alone, privately-owned-and-operated infrastructure to a shared infrastructure model. These shared infrastructures are being offered by infrastructure service providers which have subscription, or pay-on-demand, charge models presenting compute and storage resources as a generalized utility. Utility based infrastructures that are run by service providers have been defined as "cloud computing" by the National Institute of Standards and Technology. In the cloud computing model the concerns of security and privacy protections are exacerbated due to the requirement for an enterprise to allow third parties to own and manage the infrastructure and be custodians of the enterprises information. With this new architectural model, there are new hybrid governance models designed to support complex and uncertain environments. The cloud also requires a common infrastructure that integrates originally separate computing silos. Privacy and security policy awareness during provisioning and computing orchestration about data locality across domains and jurisdictions must be able to obey legal and regulatory constraints. Commercial use of the Internet for electronic commerce has been growing at a phenomenal rate while consumer concern has also risen about the information gathered about them. Concern about privacy of data has been rated as the number one barrier by all industries. The purpose of this dissertation is to perform an empirical study to determine if existing privacy assessment instruments adequately assess privacy risks when applied to cloud infrastructures. The methodology for determining this is to apply a specific set of privacy risk assessments against a three cloud environments. The assessments are run in the context of a typical web based application deployed against cloud providers that have the five key cloud tenets - on-demand/self-service, broad network access, resource pooling, rapid elasticity, and measured service.
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Baggi, Michele. "Rule-based Methodologies for the Specification and Analysis of Complex Computing Systems." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8964.

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Desde los orígenes del hardware y el software hasta la época actual, la complejidad de los sistemas de cálculo ha supuesto un problema al cual informáticos, ingenieros y programadores han tenido que enfrentarse. Como resultado de este esfuerzo han surgido y madurado importantes áreas de investigación. En esta disertación abordamos algunas de las líneas de investigación actuales relacionada con el análisis y la verificación de sistemas de computación complejos utilizando métodos formales y lenguajes de dominio específico. En esta tesis nos centramos en los sistemas distribuidos, con un especial interés por los sistemas Web y los sistemas biológicos. La primera parte de la tesis está dedicada a aspectos de seguridad y técnicas relacionadas, concretamente la certificación del software. En primer lugar estudiamos sistemas de control de acceso a recursos y proponemos un lenguaje para especificar políticas de control de acceso que están fuertemente asociadas a bases de conocimiento y que proporcionan una descripción sensible a la semántica de los recursos o elementos a los que se accede. También hemos desarrollado un marco novedoso de trabajo para la Code-Carrying Theory, una metodología para la certificación del software cuyo objetivo es asegurar el envío seguro de código en un entorno distribuido. Nuestro marco de trabajo está basado en un sistema de transformación de teorías de reescritura mediante operaciones de plegado/desplegado. La segunda parte de esta tesis se concentra en el análisis y la verificación de sistemas Web y sistemas biológicos. Proponemos un lenguaje para el filtrado de información que permite la recuperación de informaciones en grandes almacenes de datos. Dicho lenguaje utiliza información semántica obtenida a partir de ontologías remotas para re nar el proceso de filtrado. También estudiamos métodos de validación para comprobar la consistencia de contenidos web con respecto a propiedades sintácticas y semánticas. Otra de nuestras contribuciones es la propuesta de un lenguaje que permite definir y comprobar automáticamente restricciones semánticas y sintácticas en el contenido estático de un sistema Web. Finalmente, también consideramos los sistemas biológicos y nos centramos en un formalismo basado en lógica de reescritura para el modelado y el análisis de aspectos cuantitativos de los procesos biológicos. Para evaluar la efectividad de todas las metodologías propuestas, hemos prestado especial atención al desarrollo de prototipos que se han implementado utilizando lenguajes basados en reglas.<br>Baggi ., M. (2010). Rule-based Methodologies for the Specification and Analysis of Complex Computing Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8964<br>Palancia
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Kraus, Edwin. "Interworking methodologies for DCOM and CORBA." [Johnson City, Tenn. : East Tennessee State University], 2003. http://etd-submit.etsu.edu/etd/theses/available/etd-1104103-205221/unrestricted/KrausE110503b.pdf.

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Thesis (M.S.)--East Tennessee State University, 2003.<br>Title from electronic submission form. ETSU ETD database URN: etd-1104103-205221. Includes bibliographical references. Also available via Internet at the UMI web site.
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Pan, Long. "Effective and Efficient Methodologies for Social Network Analysis." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/25962.

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Performing social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism. In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology is<br>Ph. D.
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Books on the topic "Computing Methodologies"

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Huang, De-Shuang, Kang-Hyun Jo, Junfeng Jing, Prashan Premaratne, Vitoantonio Bevilacqua, and Abir Hussain, eds. Intelligent Computing Methodologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13832-4.

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Huang, De-Shuang, Abir Hussain, Kyungsook Han, and M. Michael Gromiha, eds. Intelligent Computing Methodologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2.

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Huang, De-Shuang, and Prashan Premaratne, eds. Intelligent Computing Methodologies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60796-8.

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Huang, De-Shuang, Kyungsook Han, and Abir Hussain, eds. Intelligent Computing Methodologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42297-8.

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Huang, De-Shuang, M. Michael Gromiha, Kyungsook Han, and Abir Hussain, eds. Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3.

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Huang, De-Shuang, Kang-Hyun Jo, and Ling Wang, eds. Intelligent Computing Methodologies. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09339-0.

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Huang, De-Shuang, Zhi-Kai Huang, and Abir Hussain, eds. Intelligent Computing Methodologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26766-7.

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Huang, De-Shuang, Vitoantonio Bevilacqua, and Prashan Premaratne, eds. Intelligent Computing Theories and Methodologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22180-9.

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Huang, De-Shuang, Kang-Hyun Jo, and Abir Hussain, eds. Intelligent Computing Theories and Methodologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22186-1.

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Hoffmann, Frank, Mario Köppen, Frank Klawonn, and Rajkumar Roy, eds. Soft Computing: Methodologies and Applications. Springer-Verlag, 2005. http://dx.doi.org/10.1007/3-540-32400-3.

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Book chapters on the topic "Computing Methodologies"

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Jiang, Jing, Fei Han, Qing-Hua Ling, and Ben-Yue Su. "An Improved Evolutionary Extreme Learning Machine Based on Multiobjective Particle Swarm Optimization." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_1.

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Chen, Chen, Jing-Jing Zhang, Chun-Hou Zheng, Qing Yan, and Li-Na Xun. "Classification of Hyperspectral Data Using a Multi-Channel Convolutional Neural Network." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_10.

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Yu, Nai-Kang, Rong Hu, Bin Qian, Zi-Qi Zhang, and Ling Wang. "Improved Sub-gradient Algorithm for Solving the Lower Bound of No-Wait Flow-Shop Scheduling with Sequence-Dependent Setup Times and Release Dates." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_11.

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Tan, Run-xu, Jun Zhang, Peng Chen, Bing Wang, and Yi Xia. "Cells Counting with Convolutional Neural Network." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_12.

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Han, Wen-wen, Peng Zheng, Zhong-Qiu Zhao, and Wei-dong Tian. "Improved Adaptive Incremental Error-Minimization-Based Extreme Learning Machine with Localized Generalization Error Model." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_13.

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Zhang, Bingjie, Junze Wang, Shujun Wu, Jian Wang, and Huaqing Zhang. "Fully Complex-Valued Wirtinger Conjugate Neural Networks with Generalized Armijo Search." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_14.

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Zhang, Guohui, Gaoyuan Liang, Fang Su, Fanxin Qu, and Jing-Yan Wang. "Cross-Domain Attribute Representation Based on Convolutional Neural Network." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_15.

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Han, Ting-ting, Kai-chao Miao, Ye-qing Yao, et al. "Convolutional Neural Network for Short Term Fog Forecasting Based on Meteorological Elements." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_16.

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Van Nguyen, Tan, and Cheolkeun Ha. "RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_17.

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Ali, Sarwar, Shamim Al Mamun, Hisato Fukuda, Antony Lam, Yoshinori Kobayashi, and Yoshonori Kuno. "Smart Robotic Wheelchair for Bus Boarding Using CNN Combined with Hough Transforms." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_18.

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Conference papers on the topic "Computing Methodologies"

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Song, Yubo, and Subham Sahoo. "Spike Talk in Power Electronic Grids - Leveraging Post Moore's Computing Laws." In 2024 IEEE Design Methodologies Conference (DMC). IEEE, 2024. https://doi.org/10.1109/dmc62632.2024.10812125.

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Delfin, S., N. P. Sivasanker., Nishant Raj, and Ashish Anand. "Expression of Concern for: Fog Computing: A New Era of Cloud Computing." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc44992.2019.10702739.

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Singh, Harshdeep, Sarabpreet Kaur, and Preeti Sharma. "Utilizing Various Filtering Methodologies in Digital Image Processing." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10738905.

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"Theory of computing and computing methodologies." In 28th International Conference on Information Technology Interfaces, 2006. IEEE, 2006. http://dx.doi.org/10.1109/iti.2006.1708561.

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"Theory of computing and computing methodologies." In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces (ITI). IEEE, 2009. http://dx.doi.org/10.1109/iti.2009.5196161.

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"Theory of computing and computing methodologies." In ITI 2008 - 30th International Conference on Information Technology Interfaces. IEEE, 2008. http://dx.doi.org/10.1109/iti.2008.4588533.

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Chu, William Cheng-Chung, Chih-Hung Chang, Tse-Chuan Hsu, and Yen-Hua Huang. "Applying Software Defined Methodologies to Software Computing." In 2016 Third International Conference on Trustworthy Systems and their Applications (TSA). IEEE, 2016. http://dx.doi.org/10.1109/tsa.2016.15.

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Bhatt, Rajen, Ghulam Mohiuddin Khan, Sujith Kumar, and Durga Ganesh Grandhi. "Audience analysis solution using soft computing methodologies." In Knowledge Engineering 2010). IEEE, 2010. http://dx.doi.org/10.1109/ictke.2010.5692918.

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Delfin, S., N. P. Sivasanker., Nishant Raj, and Ashish Anand. "Fog Computing: A New Era of Cloud Computing." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc.2019.8819633.

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Zhou, Lin, Houwang Zhang, and Lulu Sun. "Cloud computing and its comparisons with grid computing." In International Conference on Simulation and Modeling Methodologies, Technologies and Applications. WIT Press, 2014. http://dx.doi.org/10.2495/smta141062.

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Reports on the topic "Computing Methodologies"

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Park, Song-Jun, Dale Shires, and Brian Henz. Reconfigurable Computing: Experiences and Methodologies. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada478308.

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Li, Hai. Memristor-Based Computing Architecture: Design Methodologies and Circuit Techniques. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada582676.

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security issues in edge computing, particularly in the realm of encryption. A prominent limitation of edge devices is the hardware’s ability to support the computational complexity of traditional encryption methodologies (Alwarafy et al. 2020). Furthermore, encryption on the edge poses challenges in key management, the process by which cryptographic keys are transferred and stored among devices (Zeyu et al. 2020). Though edge computing provides reduced latency in data processing, encryption mechanism utilization reintroduces delay and can hinder achieving real-time results (Yu et al. 2018). The IoT is composed of a wide range of devices with a diverse set of computational capabilities, rendering a homogeneous solution for encryption impractical (Dar et al. 2019). Edge devices are often deployed in operational locations that are vulnerable to physical tampering and attacks. Sensitive data may be compromised if not sufficiently encrypted or if keys are not managed properly. Furthermore, the distributed nature and quantity of edge devices create a vast attack surface that can be compromised in other ways (Xiao et al. 2019). Understanding established mechanisms and exploring emerging methodologies for encryption reveals potential solutions for developing a robust solution for edge computing applications. The purpose of this document is to detail the current research for encryption methods in the edge computing space and highlight the major challenges associated with executing successful encryption on the edge.
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Garton, Timothy. Data enrichment and enhanced accessibility of waterborne commerce numerical data : spatially depicting the National Waterway Network. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/39223.

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This report provides methodologies and processes of data enrichment and enhanced accessibility of Waterborne Commerce and Statistics Center (WCSC) maintained databases. These databases house tabular and statistical data that reports on The U.S. Army Corps of Engineers (USACE) Civil Works Division National Waterway Network (NWN), which geospatially represents approximately 1,000 harbors and 25,000 miles of channels and waterways. WCSC is a division of The Institute for Water Resources (IWR). They have been tasked with the international collection, maintenance, and archival of all records involving commercial movements and commerce that occur on federal waterways. The current records structure is a large, tabular dataset and limited to the systems and processes put in place prior to the computing standards and capabilities available today. Methods have been tested and utilized to bring the tabular datasets into an optimized, modern geospatial network and expanded upon to create a higher resolution than previously maintained by the WCSC. This report will expand upon the applied methodologies to optimize data queries and the overall enhancement of the data system to allow for linkages to various other sources of information for commerce data enhancement for decision support assistance.
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Eparkhina, Dina. EuroSea Legacy Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d8.12.

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EuroSea is a holistic large-scale project encompassing the full value chain of marine knowledge, from observations to modelling and forecasting and to user-focused services. This report summarizes the legacy of EuroSea as planned and measured through a dedicated impact monitoring protocol, a holistic assessment of the project's successes in advancing and integrating European ocean observing and forecasting systems. Since its start, EuroSea has been analysing how well the project progresses towards the identified areas of impact. Impact assessment is not performance evaluation. These terms overlap but are distinct: performance relates to the efficient use of resources; impact relates to the transformative effect on the users. The EuroSea legacy report is presented through an aggregation and analysis of the EuroSea work towards achieving its impacts. Overall, over 100 impacts have been identified and presented on the website and in a stand-alone impact report. The legacy report sheds light on 32 most powerful impacts (four impacts in each of the eight EuroSea impact areas). EuroSea Impact Areas: 1. Strengthen the European Ocean Observing System (EOOS), support the Global Ocean Observing System (GOOS) and the GOOS Regional Alliances; 2. Increase ocean data sharing and integration; 3. Deliver improved climate change predictions; 4. Build capacity, internally in EuroSea and externally with EuroSea users, in a range of key areas; 5. Develop innovations, including exploitation of novel ideas or concepts; shorten the time span between research and innovation and foster economic value in the blue economy; 6. Facilitate methodologies, best practices, and knowledge transfer in ocean observing and forecasting; 7. Contribute to policy making in research, innovation, and technology; 8. Raise awareness of the need for a fit for purpose, sustained, observing and forecasting system in Europe. Ocean observing and forecasting is a complex activity brining about a variety of technologies, human expertise, in water and remote sensing measurements, high-volume computing and artificial intelligence, and a high degree of governance and coordination. Determining an impact on a user type or an area, therefore, requires a holistic assessment and a clear strategic overview. The EuroSea impact monitoring protocol has been the first known such attempt in a European ocean observing and forecasting project. The project’s progress has been followed according to the identified impact areas, through consortium workshops, stakeholder webinars, tracking, and reporting. At the end of EuroSea, we are able to demonstrate how well we have responded to the European policy drivers set out in the funding call and the grant agreement of our project, signed between the European Commission and 53 organizations, members of the EuroSea consortium. The project's impact is diverse, spanning areas from strengthening ocean observing governance to contributing to policymaking or boosting ocean research, innovation, and technology. Each impact area underscores EuroSea's commitment to a sustainable and informed approach to ocean observing and forecasting for enhanced marine knowledge and science-based sustainable blue economy and policies. (EuroSea Deliverable, D8.12)
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Disaster Risk Profile for Jamaica. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0009191.

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One of the key strategic activities of disaster risk management at country level is the assessment of disaster risk. However, few methods allow analysis of the disaster risk for two reasons. One is due to the lack of information that allows building a robust database to describe the exposure values. The other is due to the lack of methodologies for modeling the hazardous risk, the vulnerability function, the exposed values and the risk of calculation in an integrated manner. The aim of the study Disaster Risk Profile for Jamaica is to assess the country's disaster risk, using an appropriate probabilistic technique that takes into account the uncertainty of the process, the inevitable limitations on information and the available electronic computing capacity.
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