Cloud computing is nowadays becoming a popular paradigm for the provision of computing infrastructure that enables organizations to achieve financial savings. On the other hand, there are some known obstacles, among which vendor lock-in stands out. Furthermore, due to missing standards and heterogeneities of cloud storage systems, the migration of data to alternative cloud providers is expensive and time-consuming. We propose an approach based on Semantic Web services and AI planning to tackle cloud vendor data lock-in problem. To complete the mentioned task, data structures and data type mapping rules between different types of cloud storage systems are defined. The migration of data among different providers of platform as a service is presented in order to prove the practical applicability of the proposed approach. Additionally, this concept was also applied to software as a service model of cloud computing to perform one-shot data migration from Zoho CRM to Salesforce CRM.
"Sustainable development" is one of the major issues in the 21st century. Thus the notions of green computing, green development and so on show up one after another. As the large-scale parallel computing systems develop rapidly, energy consumption of such systems is becoming very huge, especially system performance reaches Petascale (10(15) Flops) or even Exascale (10(18) Flops). The huge energy consumption increases the system temperature, which seriously undermines the stability and reliability, and limits the growth of system size. The effects of energy consumption on scalability become a growing concern. Against the background, this paper proposes the concept of "Energy Wall" to highlight the significance of achieving scalable performance in peta/exascale supercomputing by taking energy consumption into account. We quantify the effect of energy consumption on scalability by building the energy-efficiency speedup model, which integrates computing performance and system energy. We define the energy wall quantitatively, and provide the theorem on the existence of the energy wall, and categorize the large-scale parallel computers according to the energy consumption. In the context of several representative types of HPC applications, we analyze and extrapolate the existence of the energy wall considering three kinds of topologies, 3D-Torus, binary n-cube and Fat tree which provides insights on how to mitigate the energy wall effect in system design and through hardware/software optimization in peta/exascale supercomputing.
Natural and man-made catastrophic events appear be steadily increasing in intensity and frequency. Proper preparation, response and recovery are essential if mankind and its vital systems are to cope with and survive large-scale disasters. The organisations responsible for delivering emergency response services often under-perform due to a lack of proper interoperation and collaboration. This paper discuss the interoperability issues for data interchange among first responders agency participating in emergency situation and provides exhaustive overview of the recent studies and attempts to solve the problem. The approach taken by the EU funded REDIRNET project for development of a core ontology enabling exchange of data among first responder agencies is presented. The novelty included in the ontology tree is described and the implementation in the meta-data gateway is introduced. The paper provides insight in the implementation of the REDIRNET platform and the designed semantic interoperable services. The paper ends with the discussion of the presented work and the concluding remarks.
This paper addresses the problem of recovering 3D human pose from single 2D images using Sparse Representation. While recent Sparse Representation (SR) based 3D human pose estimation methods have attained promising results estimating human poses from single images, their performance depends on the availability of large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but it is relatively easy to acquire a large amount of unlabeled data. Moreover, all SR based 3D pose estimation methods only consider the information of the input feature space and they cannot utilize the information of the pose space. In this paper, we propose a new framework based on sparse representation for 3D human pose estimation which uses both the labeled and unlabeled data. Furthermore, the proposed method can exploit the information of the pose space to improve the pose estimation accuracy. Experimental results show that the performance of the proposed method is significantly better than the state of the art 3D human pose estimation methods.
Multi-Agent theory which is used for communication and collaboration among focused crawlers has been proved that it can improve the precision of returned result significantly. In this paper, we proposed a new organizational structure of multi-agent for focused crawlers, in which the agents were divided into three categories, namely F-Agent (Facilitator-Agent), As-Agent (Assistance Agent) and C-Agent (Crawler-Agent). They worked on their own responsibilities and cooperated mutually to complete a common task of web crawling. In our proposed architecture of focused crawlers based on multi-agent system, we emphasized discussing the collaborative process among multiple agents. To control the cooperation among agents, we proposed a negotiation protocol based on the contract net protocol and achieved the collaboration model of focused crawlers based on multi-agent by JADE. At last, the comparative experiment results showed that our focused crawlers had higher precision and efficiency than other crawlers using the algorithms with breadth-first, best-first, etc.
By remote sensing methods natural oil seeps and their sources in the southern part of the Caspian Sea off the coast of Iran and in the Barents Sea are studied. It is shown that with the help of a geoinformational approach and additional geological-geophysical information and bathymetric data, it is possible not only to determine their actual position at the bottom, but also to obtain information on their activity, e.g., frequency, volumes of emitted oil, and oil and gas deposits. In addition, this approach allows discovering new seep sources in various seas. It is concluded that the SAR data of the European Sentinel-1 satellites is an excellent material for monitoring and studying natural oil seeping through the observation of oil slicks floating on the sea surface.
Central authority free multi-authority attribute based encryption scheme for short messages will be presented. Several multi-authority attribute based encryption schemes were recently proposed. We can divide these schemes into two groups, one of them are the ciphertext-policy attribute based encryption schemes (CP-ABE), the another one are the key-policy attribute based encryption schemes (KP-ABE). In our new multi-authority attribute based encryption scheme we combine them: the access structure will be given by authorities and the encryptor in conjunction. The authorities will be able to decide who is able to decrypt a ciphertext under their names, but the encryptor will choose the authorities whom he would involve in the encryption. In addition, our scheme is free of any central authority. The security of our new scheme relies on the decisional 3-party Diffie-Hellman assumption.
In the constantly growing blogosphere with no restrictions on form or topic, a number of writing styles and genres have emerged. Recognition and classification of these styles has become significant for information processing with an aim to improve blog search or sentiment mining. One of the main issues in this field is detection of informative and affective articles. However, such differentiation does not suffice today. In this paper we extend the differentiation and suggest a fine-grained set of subcategories for affective articles. We propose and evaluate a classification method employing novel lexical, morphological, lightweight syntactic and structural features of written text. The results show that our method outperforms the existing approaches.
Processors are unable to achieve significant gains in speed using the conventional methods. For example increasing the clock rate increases the average access time to on-chip caches which in turn lowers the average number of instructions per cycle of the processor. On-chip memory system will be the major bottleneck in future processors. Software-managed on-chip memories (SMCs) are on-chip caches where software can explicitly read and write some or all of the memory references within a block of caches. This paper footnoteThe work presented in this paper is an expansion of the authors' previously published work in the conference paper citeSMC-HPCS-10, and was carried out when the first author was a Ph.D. student in the Department of Computer Science at University of Victoria. analyzes the current trends for optimizing the use of these SMCs. We separate and compare these trends based on general classifications developed during our study. The paper not only serves as a collection of recent references, information and classifications for easy comparison and analysis but also as a motivation for improving the SMC management framework for embedded systems. It will also make a first step towards making them useful for general purpose multicore processors.
The software development approach called model-driven engineering has become increasingly widespread. The continuous integration practice has also been gaining the importance. Some works have shown that both can improve the software development process. The problem is that the model-driven engineering is still a very active research topic lacking its maturity, what translates into difficulties in optimal incorporation of the continuous integration practice in the process. We present an experience report in which we show the problems we have detected in a real project and how we have solved them. Thus, we increase the productivity of development and the non-technical people are able to modify already deployed applications. Finally, we incorporate an evaluation that shows the benefits of the proposed union.
In this article we present the design of a fast hardware simulator for P systems using the field-programmable gate array (FPGA) technology. The simulator is non-deterministic and it uses a constant time procedure to choose one of the computational paths. The obtained strategy is fair and it is based on a pre-computation of all possible rule applications. This pre-computation is obtained by using the representation of all possible multisets of rules' applications as context-free languages. Then using a standard technique involving formal power series it is possible to obtain the generating series of corresponding languages that permits to construct the structure representing all possible rule applications for any configuration. We give a hardware design implementing some concrete examples and present the obtained results which feature an important speed-up.
