Description:
This article examines trust and trust management in web services and proposes a multiagent model of trust relationship in web services. It lloks the heirarchial structure of trust managment in web services and proposes a strategic model of trust management in web services. The proposed approach in this article will facilitate research and development of trust management in e-commerce, webs services and social networking.
Description:
Water resources planning processes around the world are increasingly striving to incorporate sustainability principles in order to find optimal solutions to multi-criterial decision-making problems. The availability of sophisticated modelling techniques such as optimisation-simulation (O-S) models provide a proven approach to efficiently and effectively search for optimal solutions using trusted simulation models. However, optimisation problems in water resources planning are seldom represented using three or more objectives; the so called 'many-objective' multi-objective optimisation problems. Limiting problem dimensionality in this way simplifies visualisation of Pareto-optimal solutions, reduces computational effort, and simplifies the evaluation and interpretation of results. The aim of this study is to present a structured procedure for developing optimal operating plans for complex water resources systems using a combined multi-objective optimisation and sustainability assessment approach. The approach is applied to an 18-objective function MOOP which represents four broad categories relating to environmental, social, consumptive, and system-wide interests for water. The Wimmera-Mallee Water Supply System (WMWSS) is a multi-reservoir system located in Western Victoria (Australia) which is operated to meet a range of conflicting interests for water using complex operating rules. An O-S model is presented which uses the Elitist Non-dominated Sorting Algorithm (NSGA-II) as the optimisation engine and the REsource ALlocation Model (REALM) software as the simulation engine. This O-S model is applied to the WMWSS to search and evaluate candidate optimal operating plans over a 118-year period assuming historic hydro-climatic conditions. The resulting optimal operating plans are ranked in terms of their overall sustainability and compared to a known reference point or 'base case operating plan' which represents the status quo in operating rules for the WMWSS case study. The results show that the combined multi-objective optimisation and sustainability assessment approach is able to find optimal operating plans that are more sustainable than the base case operating plan.
Description:
The sparse nature of voluntarily reported drug safety data benefits from a system that consolidates the massive amount of data into a manageable format for analysis. This has been done for Australian drug safety data by the Australian Adverse Drug Reaction Advisory Committee (ADRAC) for reactions using the systems organ class (SOC) ontology. There has long been a need for a similar kind of grouping to apply to drugs in this type of data. In ADRAC, drugs are currently listed by trade-name, where only some of these trade-names were assigned anatomical-therapeutic-chemical classification (ATC) codes. We assigned an ATC code for each ADRAC trade-name and show that this ontology facilitates the detection of drug class / reaction class associations at various levels of specificity. This allows different views of these associations (even very rare ones) and their significance measured for the development of more sensitive signal detection methods. We report that this ATC classification enables both the grouping of association rule approach that is useful for studying rare associations, and the development of an adverse reaction signal detection method.
Description:
Previous empirical works have shown the effectiveness of differential prioritization in feature selection prior to molecular classification. We now propose to determine the theoretical basis for the concept of differential prioritization through mathematical analyses of the characteristics of predictor sets found using different values of the DDP (degree of differential prioritization) from realistic toy datasets. Mathematical analyses based on analytical measures such as distance between classes are implemented on these predictor sets. We demonstrate that the optimal value of the DDP is capable of forming a predictor set which consists of classes of features which are well separated and are highly correlated to the target classes – a characteristic of a truly optimal predictor set. From these analyses, the necessity of adjusting the DDP based on the dataset of interest is confirmed in a mathematical manner, indicating that the DDP-based feature selection technique is superior to both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods. Applying similar analyses to real-life multiclass microarray datasets, we obtain further proof of the theoretical significance of the DDP for practical applications
Description:
Swingtum stands for Swing and Momentum. A Swingtum Theory of Intelligent Finance is presented here in order to provide a scientific and engineering foundation to professional swing trading and momentum trading. The origins of Swingtum theory naturally go deep into the empirical professional technical analysis, fundamental analysis and strategic analysis, and academic quantitative analysis including financial mathematics, econophysics and computational intelligence. The central perspective of Swingtum theory is the pervasive existence of multilevel swings and abrupt momentum moves in the market prices, business fundamentals, mass psychology, and even the news flow. The dualism of swing versus momentum may resemble the wave-particle dualism in quantum mechanics, however with much higher nonlinearity and sophistication of human traders as building elements of the markets. This view forms the Swingtum Market Hypothesis, which is closer to the reality than Efficient Market Hypothesis and Fractal Market Hypothesis are. Swingtum theory models the markets with two parallel and intertwining lines of thought: the multilevel swings and momentums of a target market, and the influences from interrelated markets and the surrounding economic environment. The two lines are then unified into a comprehensive framework - Super Bayesian Influence Networks (SBIN) consisting of many Probability Ensembles of Neural Networks (PENN).
Description:
Innovation, both technological and organisational, has become the top national priority in generating strong industrial development in order to stimulate economic development and strengthen competitiveness. From this perspective, it is crucial to identify how various aspects of business management in practice are responding to the challenge of supporting innovation. One crucial aspect under scrutiny lately has been the role of human resource management (HRM) in effectively building the capacity of organisations to innovate through motivation and learning. Recent research has found a positive relationship among HRM policies, innovation and industrial performance. This important relationship has been often mentioned, but without any clear theoretical framework or empirical evidence to identify the type of HRM strategies that support innovation. The aim of this paper is to examine the Australian situation with regard to HRM strategies and their support (or lack thereof) for innovation during the 2000s boom years prior to the recent “Great Recession”. This is done by comparing two distinct HRM systems and strategies implemented in Australia. One is centred around deregulation, the other is centred around regulation. A theoretical framework is developed based on the capability of firms to innovate and how it is strictly related to their ability to substitute between labour inputs, within these two strategies. This framework then provides the basis for examining HRM practices and industrial relations systems in order to identify the difference between the learning practices that are common to successful innovation, and the ones that hamper innovation. The focus of the paper is on the input of innovative individuals. For it is individuals who learn within a frame of reference created by their education and by their social and organisational systems of rewards. The paper will show how through HRM strategies, the process of learning can lead to innovation, but it can also hinder innovation. It is crucial that societies invest in practices that foster and maintain the individual’s motivation to innovate and ability to generate new knowledge.
Description:
Innovation, both technological and organisational, has become the top national priority in generating strong industrial development in order to stimulate economic development and strengthen competitiveness. From this perspective, it is crucial to identify how various aspects of business management in practice are responding to the challenge of supporting innovation. One crucial aspect under scrutiny lately has been the role of human resource management (HRM) in effectively building the capacity of organisations to innovate through motivation and learning. Recent research has found a positive relationship among HRM policies, innovation and industrial performance. This important relationship has been often mentioned, but without any clear theoretical framework or empirical evidence to identify the type of HRM strategies that support innovation. The aim of this paper is to examine the Australian situation with regard to HRM strategies and their support (or lack thereof) for innovation during the 2000s boom years prior to the recent “Great Recession”. This is done by comparing two distinct HRM systems and strategies implemented in Australia. One is centred around deregulation, the other is centred around regulation. A theoretical framework is developed based on the capability of firms to innovate and how it is strictly related to their ability to substitute between labour inputs, within these two strategies. This framework then provides the basis for examining HRM practices and industrial relations systems in order to identify the difference between the learning practices that are common to successful innovation, and the ones that hamper innovation. The focus of the paper is on the input of innovative individuals. For it is individuals who learn within a frame of reference created by their education and by their social and organisational systems of rewards. The paper will show how through HRM strategies, the process of learning can lead to innovation, but it can also hinder innovation. It is crucial that societies invest in practices that foster and maintain the individual’s motivation to innovate and ability to generate new knowledge.
Description:
WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
Description:
WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
Description:
The concept of friend and friendship are critical to both theoretical and empirical studies of social relations, social media and social networks. Measuring the closeness among friends is a big issue for developing online social networking services (SNS) such as Facebook. This paper will address this issue by proposing a technique for ranking friendship closeness in SNS. The technique consists of an algorithm for ranking need-driven friendship closeness and an algorithm for behaviour-based friendship closeness in online social networking sites. The former is based on Maslow’s hierarchy of needs, while the latter is based on behaviours of users on Facebook and TOPSIS. Examples provided illustrate the viability of the proposed algorithms. The research in this paper shows that ranking friendship closeness will facilitate understanding of needs and behaviours of friends and of friendships in SNS. The proposed approach will facilitate research and development of social media, social commerce, social networks, and SN
Description:
The concept of friend and friendship are critical to both theoretical and empirical studies of social relations, social media and social networks. Measuring the closeness among friends is a big issue for developing online social networking services (SNS) such as Facebook. This paper will address this issue by proposing a technique for ranking friendship closeness in SNS. The technique consists of an algorithm for ranking need-driven friendship closeness and an algorithm for behaviour-based friendship closeness in online social networking sites. The former is based on Maslow’s hierarchy of needs, while the latter is based on behaviours of users on Facebook and TOPSIS. Examples provided illustrate the viability of the proposed algorithms. The research in this paper shows that ranking friendship closeness will facilitate understanding of needs and behaviours of friends and of friendships in SNS. The proposed approach will facilitate research and development of social media, social commerce, social networks, and SN