Interoperable and transparent dynamic deployment of web services for service oriented grids

Dynamic deployment of Web services is a term used frequently when describing the selection and deployment of a service to a grid host. Although current grid systems (such as Globus) provide dynamic deployment, the requirements of the service being deployed are not considered. Therefore truly dynamic deployment cannot be achieved as the services deployed are restricted to the grid system used. We present a dynamic deployment mechanism as part of self configuration in a service oriented grid environment. The dynamic deployment mechanism takes the requirements of the service into consideration, including parameters such as the operating system required to execute the service, the required software libraries, any additional required software packages, price and Quality of Service (QoS) parameters.

Attributed publication and selection for web service-based distributed systems

With the emergence of cloud computing, the need for flexible and detailed publication and selection of services that expose cloud resources is greatly stressed. While dynamic attributes have improved the publication and selection of resources in distributed systems, the use of dynamic attributes is yet to be tried in Web services: a key element that makes cloud computing possible. We propose a new approach to Web service publication and selection using dynamic attributes shown in Web service WSDL documents, the most commonly accessed and used elements of Web services.

Phishing email feature selection approach

Phishing emails are more dynamic and cause high risk of significant data, brand and financial loss to average computer user and organizations. To address this problem, we propose a hybrid feature selection approach based on combination of content-based and behavior-based. Our proposed hybrid features selections are able to achieve 93% accuracy rate as compared to other approaches. In addition, we successfully tested the quality of our proposed behavior-based feature using the Information Gain, Gain Ratio and Symmetrical Uncertainty.

Dynamic currency hedging for international stock portfolios

The paper studies dynamic currency risk hedging of international stock portfolios using a currency overlay. A dynamic conditional correlation (DCC) multivariate GARCH model is employed to estimate time-varying covariance among stock market returns and currency returns. The conditional covariance is then used in the estimation of risk-minimizing conditional hedge ratios. The study considers seven developed economies over the period January 2002 to April 2010 and estimates daily conditional hedge ratios for portfolios of various stock market combinations. Conditional hedging is shown to dominate traditional static hedging and unconditional hedging in terms of risk reduction both in-sample and out-of-sample, especially during the recent global financial crisis. Conditional hedging also proves to consistently reduce portfolio risk for various levels of foreign investments.

Dynamic path determination policy for distributed multimedia content adaptation

Multimedia content adaptation allows the ever increasing variety of handheld devices such as Smartphones to access distributed rich media resources available on the Internet today. Path planning and determination is a fundamental problem in enhancing performance of distributed multimedia content adaptation systems. Most of the existing path determination mechanisms use static path determination criteria based solely on associating a path with a single behavior aggregate score. However, some criteria such as availability are best represented using different functionality rather than being accumulated into the aggregate score. Moreover, since selection criteria have different behavior towards the score, this principle need to be considered. In this paper, we propose a dynamic multi-criteria path determination policy that selects an optimal path to the content adaptation services that best meet the user preferences and QoS requirements. The performance of the proposed approach is studied in terms of score’s fairness and reliability under different variations. The results indicate that the proposed policy performs substantially better than the baseline policy.

In hot pursuit : fluctuating mating system and sexual selection in sand lizards

A changing climate is expected to have profound effects on many aspects of ectotherm biology. We report on a decade-long study of free-ranging sand lizards (Lacerta agilis), exposed to an increasing mean mating season temperature and with known operational sex ratios. We assessed year-to-year variation in sexual selection on body size and postcopulatory sperm competition and cryptic female choice. Higher temperature was not linked to strength of sexual selection on body mass, but operational sex ratio (more males) did increase the strength of sexual selection on body size. Elevated temperature increased mating rate and number of sires per clutch with positive effects on offspring fitness. In years when the “quality” of a female's partners was more variable (in standard errors of a male sexual ornament), clutches showed less multiple paternity. This agrees with prior laboratory trials in which females exercised stronger cryptic female choice when male quality varied more. An increased number of sires contributing to within-clutch paternity decreased the risk of having malformed offspring. Ultimately, such variation may contribute to highly dynamic and shifting selection mosaics in the wild, with potential implications for the evolutionary ecology of mating systems and population responses to rapidly changing environmental conditions.

Designing an e-portfolio for assurance of learning focusing on adoptability and learning analytics

The Assurance of Learning for Graduate Employability framework is a quality assurance model for curriculum enhancement for graduate employability, enabling graduates to achieve "the skills, understandings and personal attributes that make [them] more likely to secure employment and be successful in their chosen occupations to the benefit of themselves, the workforce, the community and the economy" (Yorke, 2006). Of particular note is the framework's dependence on three foundations, including easy access to integrated and accessible tools for staff and student self-management. In other words, this approach to curriculum quality depends on staff and student access to tools that enable them to self-manage their learning. This paper examines two aspects which informed the design of a student e-portfolio system, iPortfolio, intended for students' self-management of their learning, particularly recording evidence of their achievement of capabilities. The paper focuses on two particular considerations in the design of the iPortfolio: adoptability and learning analytics. Adoptability means the phase preceding adoption, whether students have the devices, platforms and technology skills to be able to use such an innovation. The iPortfolio also facilitates learning analytics: it has the capability to gather data related to learning indicators for course quality assurance purposes. Both adoptability and analytics are very dynamic fields: new devices, platforms and applications constantly spark changes in user habits, and policy changes mean institutions need to be able to provide new data, often at short notice. In the conclusion, the paper suggests how tools such as the iPortfolio can be designed for 'future proofing' and sustainability.

Fuzzy portfolio allocation models through a new risk measure and fuzzy sharpe ratio

A new portfolio risk measure that is the uncertainty of portfolio fuzzy return is introduced in this paper. Beyond the well-known Sharpe ratio (i.e., the reward-to-variability ratio) in modern portfolio theory, we initiate the so-called fuzzy Sharpe ratio in the fuzzy modeling context. In addition to the introduction of the new risk measure, we also put forward the reward-to-uncertainty ratio to assess the portfolio performance in fuzzy modeling. Corresponding to two approaches based on TM and TW fuzzy arithmetic, two portfolio optimization models are formulated in which the uncertainty of portfolio fuzzy returns is minimized, while the fuzzy Sharpe ratio is maximized. These models are solved by the fuzzy approach or by the genetic algorithm (GA). Solutions of the two proposed models are shown to be dominant in terms of portfolio return uncertainty compared with those of the conventional mean-variance optimization (MVO) model used prevalently in the financial literature. In terms of portfolio performance evaluated by the fuzzy Sharpe ratio and the reward-to-uncertainty ratio, the model using TW fuzzy arithmetic results in higher performance portfolios than those obtained by both the MVO and the fuzzy model, which employs TM fuzzy arithmetic. We also find that using the fuzzy approach for solving multiobjective problems appears to achieve more optimal solutions than using GA, although GA can offer a series of well-diversified portfolio solutions diagrammed in a Pareto frontier.

A distributed gateway selection algorithm for UAV networks

In recent years, unmanned aerial vehicle (UAV) has been widely adopted in military and civilian applications. For small UAVs, cooperation based on communication networks can effectively expand their working area. Although the UAV networks are quite similar to the traditional mobile ad hoc networks, the special characteristics of the UAV application scenario have not been considered in the literature. In this paper, we propose a distributed gateway selection algorithm with dynamic network partition by taking into account the application characteristics of UAV networks. In the proposed algorithm, the influence of the asymmetry information phenomenon on UAVs' topology control is weakened by dividing the network into several subareas. During the operation of the network, the partition of the network can be adaptively adjusted to keep the whole network topology stable even though UAVs are moving rapidly. Meanwhile, the number of gateways can be completely controlled according to the system requirements. In particular, we define the stability of UAV networks, build a network partition model, and design a distributed gateway selection algorithm. Simulation results show using our proposed scheme that the faster the nodes move in the network, the more stable topology can be found, which is quite suitable for UAV applications.

Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of work-flow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of “necessity and sufficiency” are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

Role of index bonds in an optimal dynamic asset allocation model with real subsistence consumption

We investigate the role of index bonds in a dynamic consumption and asset allocation model where the rate of real consumption at any given time cannot fall below a fixed level. An explicit form of the optimal consumption and portfolio rule for a class of Constant Relative Risk Aversion (CRRA) utility functions is derived. Consumption increases above the subsistence level only when wealth exceeds a threshold value. Risky investments in equity and nominal bonds are initially proportional to the excess of wealth over a lower bound, and then increase nonlinearly with wealth. The desirability of investing in the risky assets are related to the agent’s risk preference, the equity premium, and the inflation risk premium. The demand for index bonds is also obtained. The results should be useful for the management of defined benefit pension funds, university endowments, and other portfolios which have a withdrawal pre-commitment in real terms.