Agents and data mining: mutual enhancement by integration

This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.

Management framework for corporate telecommunication and data centre information security

Network security, particularly Internet security, is at the forefront of business and government networks. This research has discovered weaknesses in current professional practice, particularly in mitigation strategies to reduce the impacts of security violations in corporate telecommunications and data centres. The importance of integrating security policies, processes and operational practice is demonstrated. Leadership models and innovation mechanisms best suited to improved security design are also identified.

Subjective wellbeing and families : issues of measurement and data interpretation

Since April 2001 we have been monitoring the Subjective Wellbeing (SWB) of the Australian population using the Personal Wellbeing Index. Our aims are to establish normative values and to identify people with abnormally low SWB. Each of 18 surveys has involved a new sample of 2,000 people, randomly chosen but representing the geographical distribution of the population. The data are remarkable for their stability, with the variation in population mean scores being just 3.2 percentage points. The cause of such high reliability is Subjective Wellbeing Homeostasis. Here, in a manner analogous to the management of body temperature, the SWB for each person is normally held positive and within a narrow set-point range. However, all homeostatic systems have a limited capacity to absorb challenge and when aversive experiences are both strong and sustained, homeostasis fails. If this occurs, people lose their normal positive view of themselves and become depressed. Therefore, the second aim of these studies is to reveal the demographic character of families in distress, who are in need of additional resources. Our data reveal the extent to which family structure and responsibilities impact on wellbeing. They also yield important diagnostic information about individuals, and point to SWB as a crucial measure of intervention outcome. In sum, the Personal Wellbeing Index is a simple, reliable and valid measure of SWB. The measures it yields are theoretically embedded, they can be compared against solid normative data, and their interpretation is enhanced through an understanding of SWB homeostasis.

Technological innovations and m-commerce applications

Many technological innovations have occurred within the past decade that have revolutionized the banking industry. The most important has been the Internet and the emergence of mobile-commerce (m-commerce). The aim of this paper is to investigate how Wireless Application Protocol (WAP) banking is being implemented with younger adopters. A conceptual model is developed based on the behavioral intentions younger adopters have on WAP banking that is tested through a survey instrument. Social cognitive theory underpins the conceptual model and helps to explain some of the findings from the study. Suggestions for future research are also espoused.

Animal migration : linking models and data beyond taxonomic limits

An international workshop on animal migration was held at the Lorentz Center in Leiden, The Netherlands, 2–6 March 2009, bringing together leading theoreticians and empiricists from the major migratory taxa, aiming at the identification of cutting-edge questions in migration research that cross taxonomic borders.

An introduction to compressive sensing and its potential applications in structural engineering

The Shannon/Nyquist sampling theorem specifies that to avoid losing information when capturing a signal, one must sample at least two times faster than the signal bandwidth. In order to capture and represent compressible signals at a rate significantly below the Nyquist rate, a new method, called compressive sensing (CS), is therefore proposed. CS theory asserts that one can recover certain signals from far fewer samples or measurements than traditional methods use. It employs non-adaptive linear projections that preserve the structure of the sparse signal; the signal is then reconstructed from these projections using an optimization process. It is believed that CS has far reaching implications, while most publications concentrate on signal processing fields (especially for images). In this paper, we provide a concise introduction of CS and then discuss some of its potential applications in structural engineering. The recorded vibration time history of a steel beam and the wave propagation result on a steel rebar are studied in detail. CS is adopted to reconstruct the time histories by using only parts of the signals. The results under different conditions are compared, which confirm that CS will be a promising tool for structural engineering.

Computation and construction of vault geometry prototypes

Physical models and scaled prototypes of architecture play an important role in design. They enable architects and designers to investigate the formal, functional, and material attributes of the design. Understanding digital processes of realizing scaled prototypes is a significant problem confronting design practice. This paper reports on three approaches to the translation of Gaussian surface models into scaled physical prototype models. Based on the geometry of Eladio Dieste’s Gaussian Vaults, the paper reports on the aspects encountered in the process of digital to physical construction using scaled prototypes. The primary focus of the paper is on computing the design geometry, investigating methods for preparing the geometry for fabrication and physical construction. Three different approaches in the translation from digital to physical models are investigated: rapid prototyping, two-dimensional surface models in paper and structural component models using CNC fabrication. The three approaches identify a body of knowledge in the design and prototyping of Gaussian vaults. Finally the paper discusses the digital to fabrication translation processes with regards to the characteristics, benefits and limitations of the three approaches of prototyping the ruled surface geometry of Gaussian Vaults. The results of each of three fabrication processes allowed for a better understanding of the digital to physical translation process. The use of rapid prototyping permits the production of form models that provide a representation of the physical characteristics such as size, shape and proportion of the Gaussian Vault.

The impact of sample size and data quality to classification reliability

The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors [3]. In some cases, the reliability could also be affected by knowledge oriented factors. In this chapter, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from low quality data, rough classification approach is more reliable than exact approach which in general tolerate to low quality data; (2) Without sufficient large size of the data, the reliability of the discovered knowledge will be decreased accordingly; (3) The reliability of point learning approach could easily be misled by noisy data. It will in most cases generate an unreliable interval and thus affect the reliability of the discovered knowledge. It is also reveals that the inexact field is a good learning strategy that could model the potentials and to improve the discovery reliability.

Downstream processing of stevioside and its potential applications

Stevioside is a natural sweetener extracted from leaves of Stevia rebaudiana Bertoni, which is commercially produced by conventional (chemical/physical) processes. This article gives an overview of the stevioside structure, various analysis technique, new technologies required and the advances achieved in recent years. An enzymatic process is established, by which the maximum efficacy and benefit of the process can be achieved. The efficiency of the enzymatic process is quite comparable to that of other physical and chemical methods. Finally, we believe that in the future, the enzyme-based extraction will ensure more cost-effective availability of stevioside, thus assisting in the development of more food-based applications.