9,628 research outputs found
A web assessment approach based on summarisation and visualisation
The number of Web sites has noticeably increased to roughly 224 million in last ten years. This means there is a rapid growth of information on the Internet. Although search engines can help users to filter their desired information, the searched result is normally presented in the form of a very long list, and users have to visit each Web page in order to determine the appropriateness of the result. This leads to a considerable amount of time has to be spent on finding the required information. To address this issue, this paper proposes a Web assessment approach in order to provide an overview of the information on a Website using an integration of existing summarisation and visualisation techniques, which are text summarisation, tag cloud, Document Type View, and interactive features. This approach is capable to reduce the time required to identify and search for information from the Web
Summarizing information from Web sites on distributed power generation and alternative energy development
The World Wide Web (WWW) has become a huge repository of information and knowledge, and an essential channel for information exchange. Many sites and thousands of pages of information on distributed power generation and alternate energy development are being added or modified constantly and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some forms of ranking mechanism, it is still necessary to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process by providing a summary of the information on the Web site. The proposed approach makes use of graphical visualization, tag clouds and text summarization. A number of cases are presented and compared in this paper with a discussion on future work
A model for mobile content filtering on non-interactive recommendation systems
To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems
Hedge fund replication strategies: implications for investors and regulators.
Over the past decade, academic research has identified a number of replication strategies capable of capturing between 40% to 80% of the average return of many popular hedge fund strategies. Investors are beginning to take notice of these replication strategies, especially because of their rule based, transparent features and the fact that they can be executed at low cost. Armed with this alternative way of accessing passive hedge fund returns, investors can effectively structure incentive fee contracts to reward skill-based returns (i.e., alternative alpha) differently from passive index-liked returns (i.e., alternative beta). This can raise the barrier to entry for new funds to the industry in that hedge fund managers must demonstrate skill in order to participate in profi t sharing. This should reduce the risk of herding by hedge fund managers who may otherwise be enticed by incentive fee contracts that rewards them for taking popular factor bets.
Atmospheric, climatic and environmental research
Research conducted during the past year in the climate and atmospheric modeling programs was focused on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Prinicpal models are a one-dimensional radiative-convection model, a three-dimensional global climate model, and an upper ocean model. Principal application is the study of the impact of CO2, aerosols and the solar constant on climate. Also the performance of the International Satellite Cloud Climatology Project cloud detection algorithm was evaluated, concentrating initially on its application to geosynchronous data, with an eventual switch of the developed methodologies to data from polar orbiting satellites. In the process, a number of improvements were made, in particular: an improved technique for tracking small scale day to day variability in clear sky continental temperatures; a number of techniques for the statistical assessment of cloud detection uncertainties due to cloud types which are spatially and temporally invariant; and a method used to detect those cloudy regions which have long term spatial and temporal stability
An intelligent recommendation system framework for student relationship management
In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification
Formation of Non-reciprocal Bands in Magnetized Diatomic Plasmonic Chains
We show that non-reciprocal bands can be formed in a magnetized periodic
chain of spherical plasmonic particles with two particles per unit cell.
Simplified form of symmetry operators in dipole approximations are used to
demonstrate explicitly the relation between spectral non-reciprocity and broken
spatial-temporal symmetries. Due to hybridization among plasmon modes and free
photon modes, strong spectral non-reciprocity appears in region slightly below
the lightline, where highly directed guiding of energy can be supported. The
results may provide a clear guidance on the design of one-way waveguides
A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing
In this article we investigate a state-space representation of the Lee-Carter
model which is a benchmark stochastic mortality model for forecasting
age-specific death rates. Existing relevant literature focuses mainly on
mortality forecasting or pricing of longevity derivatives, while the full
implications and methods of using the state-space representation of the
Lee-Carter model in pricing retirement income products is yet to be examined.
The main contribution of this article is twofold. First, we provide a rigorous
and detailed derivation of the posterior distributions of the parameters and
the latent process of the Lee-Carter model via Gibbs sampling. Our assumption
for priors is slightly more general than the current literature in this area.
Moreover, we suggest a new form of identification constraint not yet utilised
in the actuarial literature that proves to be a more convenient approach for
estimating the model under the state-space framework. Second, by exploiting the
posterior distribution of the latent process and parameters, we examine the
pricing range of annuities, taking into account the stochastic nature of the
dynamics of the mortality rates. In this way we aim to capture the impact of
longevity risk on the pricing of annuities. The outcome of our study
demonstrates that an annuity price can be more than 4% under-valued when
different assumptions are made on determining the survival curve constructed
from the distribution of the forecasted death rates. Given that a typical
annuity portfolio consists of a large number of policies with maturities which
span decades, we conclude that the impact of longevity risk on the accurate
pricing of annuities is a significant issue to be further researched. In
addition, we find that mis-pricing is increasingly more pronounced for older
ages as well as for annuity policies having a longer maturity.Comment: 9 pages; conference pape
On the theory of polarization transfer in inhomogeneous magnetized plasmas
Polarization transfer theory in inhomogeneous magnetized plasmas with mode couplin
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