In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

https://tandfonline.com/doi/pdf/10.1080/17538947.2016.1239771

Big Data and cloud computing: innovation opportunities and challenges

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

/pdf/an-improved-ant-colony-optimization-algorithm-based-on-1yjw4mm9u3.pdf

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

Overcoming catastrophic forgetting in neural networks

Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com .

Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy

As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply This poses new challenges to the service matching between supply-side and demand-side In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework The results demonstrate that our method has a substantial promise

Social Learning Evolution (SLE): Computational Experiment-Based Modeling Framework of Social Manufacturing

https://isiarticles.com/bundles/Article/pre/pdf/42433.pdf

Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

Value Entropy: A Systematic Evaluation Model of Service Ecosystem Evolution

Web service dynamic composition is a key technology for creating value-added services by composing available services and applications. With the rapid development of web service, cloud computing, big data and internet of things, more and more services with identical functionality and different Quality of Service (QoS) are available; moreover, QoS of Web services are highly dynamic, so, how to create composite Web services reliably and efficiently is still an open issue. For this problem, this paper proposes a reliable Web service composition method based on global QoS constraints decomposition and QoS dynamic prediction. The approach includes two critical phases: firstly, before service composition, global QoS constraints are decomposed into local constraints, and the problem of Web service dynamic composition is transformed to a local optimization problem; secondly, during the running time, optimal Web service is selected for the current abstract service based on predicted QoS values. Experiment results show that our approach can greatly enhance the reliability of composite Web service.

Xiao Xue

Papers

Social Learning Evolution (SLE): Computational Experiment-Based Modeling Framework of Social Manufacturing

Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition

Value Entropy: A Systematic Evaluation Model of Service Ecosystem Evolution

Reliable Web service composition based on QoS dynamic prediction

Manufacturing service composition method based on networked collaboration mode