Accounting measurement in the religious perspective : conservatism or optimism

This paper concerns about accounting measurements from the socio cultural values’ perspectives. By applying Hofstede (1980), Gray (1988), and Perera (1989) studies, first of all, the study develops a theory to concern about accounting values in a religious perception. Then Islam, as one of the most debatable religion in the world, is considered as an instance. Islamic compliance accounting measurement has developed in next stage to understand how the accounting value could be different from this viewpoint from the Western conservative historical cost. This study theoretically proves that Islamic compliance accounting measurement conforms to the exit price method which is different from the Western complaisance accounting measurement.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Contextualising neo-Islamism

How do we engage with the pressing challenges of xenophobia, radicalism and security in the age of the "war on terror"? The widely felt sense of insecurity in the West is shared by Muslims both within and outside Western societies. Growing Islamic militancy and resulting increased security measures by Western powers have contributed to a pervasive sense among Muslims of being under attack (both physically and culturally). Islam and Political Violence brings together the current debate on the uneasy and potentially mutually destructive relationship between the Muslim world and the West and argues we are on a dangerous trajectory, strengthening dichotomous notions of the divide between the West and the Muslim world.

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Veiled threats : recurrent cultural anxieties in Australia

At the end of the nineteenth century, white Australians found themselves in a turbulent and rapidly changing world. As British settlers in a vast, often-perplexing and under-populated continent, they were increasingly aware that they lived in a crowded and predominantly Asian neighbourhood. Their supposedly empty spaces seemed to invite the unwanted attention of hostile outsiders, fertile soil for speculation about vulnerable borders, invasion and violation. It was commonplace of the period for white females to be considered at once particularly vulnerable and also innocent symbols of the new nation. They needed to be protected against Asian males allegedly bent on conquest and violation. It does not follow that these “invasion narratives”, however persistent, meant that the entire population was disabled by fear and dread, but there is convincing evidence of a deeply embedded cultural anxiety about the destructive possibilities and hostile intentions of Asian outsiders. In this article, the authors examine recent representations of Muslims as hostile outsiders in Australia, focusing in particular on the veil as a marker of female oppression under Islam and a sign of the threat attributed to the Islamic community in Australia. While it would be misleading to propose a simple line of progression from late nineteenth century apprehensions to those a century or more later, there are nonetheless intriguing parallels and recurrent expressions of survivalist anxiety across the period examined in this article.

Dynamic feature selection for spam filtering using support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.

Do retail option traders know better about market volatility?

This paper examines the informational content and predictive power of implied volatility over different forecasting horizons in a sample of European covered warrants traded in the Hong Kong and Singapore markets. The empirical results show that time-series-based volatility forecasts outperform implied volatility forecast as a predictor of future volatility. The finding also suggests that implied volatility is biased and informationally inefficient. The results are attributable to the fact in Hong Kong and Singapore the covered warrants markets are dominated by retail investors, who tend to use covered warrants' leverage to speculate on the price movements of the underlying rather than to express their view on volatility.

Email categorization using multi-stage classification technique

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

An innovative analyser for email classification based on grey list analysis

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

Minimization of reversible adder circuits

Losing information causes losing power. Information is lost when the input vector cannot be uniquely recovered from the output vector of a combinational circuit. The input vector of reversible circuit can be uniquely recovered from the output vector. In this study we have emphasized on the design of reversible adder circuits that is efficient in terms of gate count, garbage outputs and quantum cost and that can be technologically mapped. It has been analyzed and demonstrated that the results of our proposed adder circuits shows better performance compared to similar type of existing designs. Technology independent equations required to evaluate these circuits have also been given.

The welfare economics of measuring sustainability: a new approach based on social choice theory and systems analysis

This paper presents a new measure of sustainability within a welfare economics framework. Gross domestic product (GDP) can be used as an indicator of sustainability if the GDP estimates are undertaken within a cost-benefit analysis framework based on social choice perspectives. Sustainability is dependent on a healthy and functioning socio-economic and environmental (SEE) system. Economic development can damage the SEE system through resource degradation, over-harvesting and pollution. This paper addresses the tensions between economic development and sustainability by undertaking a number of SEE-based adjustments to GDP based on social choice perspectives in order to measure sustainability. These adjustments include the environmental and social costs caused by economic development such as water pollution, the depletion of non-renewable resources, and deforestation. Thailand is used as a case study for a 25 year period (1975-1999). The results show a divergence in terms of GDP per capita and the SEE-adjusted GDP per capita figure. The paper concludes that, with increasing environmental and social costs of economic development, pursuing such extreme high growth objectives without due environmental and social considerations can threaten present social welfare and future sustainability. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment.

Making long-term economic growth more sustainable: evaluating the costs and benefits

Currently, traditional development issues such as economic stagnation, poverty, hunger, and illness as well as newer challenges like environmental degradation and globalisation demand attention. Sustainable development, including its economic, environmental and social elements, is a key goal of decisionmakers. Optimal economic growth has also been a crucial goal of both development theorists and practitioners. This paper examines the conditions under which optimal growth might be sustainable, by assessing the costs and benefits of growth. Key environmental and social aspects are considered. The Ecol-Opt-Growth-1 model analyses economic–ecological interactions, including resource depletion, pollution, irreversibility, other environmental effects, and uncertainty. It addresses some important issues, including savings, investment, technical progress, substitutability of productive factors, intergenerational efficiency, equity, and policies to make economic growth more sustainable—a basic element of the sustainomics framework. The empirical results support growing concerns that costs of growth may outweigh its benefits, resulting in unsustainability. Basically, in a wide range of circumstances, long term economic growth is unsustainable due to increasing environmental damage. Nevertheless, the model has many options that can be explored by policy makers, to make the development path more sustainable, as advocated by sustainomics. One example suggests that government supported abatement programs are needed to move towards sustainable development, since the model runs without abatement were infeasible. The optimal rate of abatement increases over time. Abatement of pollution is necessary to improve ecosystem viability and increase sustainability. Further research is necessary to seek conditions under which alternative economic growth paths are likely to become sustainable.