Government food service policies and guidelines do not create healthy school canteens

Objective
In 2006, the Victorian Government adopted the School Canteens and other school Food Services (SCFS) Policy that bans the sale of sweet drinks and confectionary and recommends the proportions of menu items based on a traffic light system of food classification. This study aims to determine whether compliance with the policy improves the nutritional profile of the menus.
Methods
Items from food service menus were assessed for compliance with the SCFS policy and categorised as ‘everyday’ (‘green’), ‘select carefully’ (‘amber’) or ‘occasionally’ (‘red’) (n=106). Profile analysis assessed differences in the nutritional profile of the menus between sub-groups.
Results
Overall, 37% of menus contained items banned under the policy. The largest proportion of items on the assessed menus were from the ‘amber’ category (mean: 51.0%), followed by ‘red’ (29.3%) and ‘green’ (20.3%). No menus met the traffic light-based recommendations and there was no relationship between policy compliance and the proportion of items in each of the three categories.
Conclusions and implications
To increase the healthiness of the school food service we recommend a greater investment in resources and infrastructure to implement existing policies, and establishing stronger monitoring and support systems.

Policy change to create supportive environments for physical activity and healthy eating : which options are the most realistic for local government?

The objective is to identify and test regulatory options for creating supportive environments for physical activity and healthy eating among local governments in Victoria, Australia. A literature review identified nine potential areas for policy intervention at local government level, including the walking environment and food policy. Discussion documents were drafted which summarized the public health evidence and legal framework for change in each area. Levels of support for particular interventions were identified through semi-structured interviews conducted with key informants from local government. We conducted 11 key informant interviews and found support for policy intervention to create environments supportive of physical activity but little support for policy changes to promote healthy eating. Participants reported lack of relevance and competing priorities as reasons for not supporting particular interventions. Promoting healthy eating environments was not considered a priority for local government above food safety. There is a real opportunity for action to prevent obesity at local government level (e.g. mandate the promotion of healthy eating environments). For local government to have a role in the promotion of healthy food environments, regulatory change and suitable funding are required.

The mutual obligation policy in Australia : the rhetoric and reasoning of recent social security policy

Since 1997, the Australian Federal Liberal Government has introduced policies which have sought to reduce rates of unemployment, particularly long-term unemployment. The policy, known as Mutual Obligation, increased the expectations on unemployed people in return for their social security payment. At the same time, previous labour market programmes and government assistance schemes were scrapped or privatised. This article explores the justification of the term 'Mutual Obligation' by examining both the language and the underlying principles of the policy. By defining the problem of unemployment in terms of flaws in the previous social security system, the stage is set for the government to introduce policies which remedy those flaws by emphasising self- reliance in favour of government assistance. Further, by invoking notions of fairness and mutuality, the article argues that the term 'Mutual Obligation' masks both the extent and the strength of the obligations imposed on unemployed people.

Pakistan’s twin interrelated challenges : economic development and security

The 2010 devastating floods in Pakistan have starkly reminded the world of the two critical, interrelated challenges confronting Pakistan: economic development and security. And whilst the Pakistan government's capacity to deal with these two issues before the flood was already shaky at best, its position now is even more precarious given the enormity of the task of rebuilding the infrastructure that has been destroyed in this latest natural disaster. Nuclear-armed Pakistan is a large and strategically important country, critically located on one of the world's most important geopolitical crossroads. It is a pivotal player in a region—covering the Middle East, Central Asia and South Asia—which has much potential, but which also has unresolved conflicts and various degrees of instability. Accordingly, because Pakistan is so important to the stability of the region and the world at large, it is vital that it be able to address successfully these twin challenges.

Microphone identification using one-class classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

An application of novel clustering technique for information security

This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on the particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.

A Grobner-Shirshov algorithm for applications in internet security

The design of multiple classification and clustering systems for the detection of malware is an important problem in internet security. Grobner-Shirshov bases have been used recently by Dazeley et al. [15] to develop an algorithm for constructions with certain restrictions on the sandwich-matrices. We develop a new Grobner Shirshov algorithm which applies to a larger variety of constructions based on combinatorial Rees matrix semigroups without any restrictions on the sandwich matrices.

Classification of correlated internet traffic flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.

Internet traffic classification by aggregating correlated naive bayes predictions

This paper presents a novel traffic classification scheme to improve classification performance when few training data arc available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.

Network traffic classification using correlation information

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

Malwise-an effective and efficient classification system for packed and polymorphic malware

Signature-based malware detection systems have been a much used response to the pervasive problem of malware. Identification of malware variants is essential to a detection system and is made possible by identifying invariant characteristics in related samples. To classify the packed and polymorphic malware, this paper proposes a novel system, named Malwise, for malware classification using a fast application-level emulator to reverse the code packing transformation, and two flowgraph matching algorithms to perform classification. An exact flowgraph matching algorithm is employed that uses string-based signatures, and is able to detect malware with near real-time performance. Additionally, a more effective approximate flowgraph matching algorithm is proposed that uses the decompilation technique of structuring to generate string-based signatures amenable to the string edit distance. We use real and synthetic malware to demonstrate the effectiveness and efficiency of Malwise. Using more than 15,000 real malware, collected from honeypots, the effectiveness is validated by showing that there is an 88 percent probability that new malware is detected as a variant of existing malware. The efficiency is demonstrated from a smaller sample set of malware where 86 percent of the samples can be classified in under 1.3 seconds.

An effective network traffic classification method with unknown flow detection

Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.

Using feature selection and classification scheme for automating phishing email detection

Email has become the critical communication medium for most organizations. Unfortunately, email-born attacks in computer networks are causing considerable economic losses worldwide. Exiting phishing email blocking appliances have little effect in weeding out the vast majority of phishing emails. At the same time, online criminals are becoming more dangerous and sophisticated. Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. In this paper, we propose a hybrid feature selection approach based combination of content-based and behaviour-based. The approach could mine the attacker behaviour based on email header. On a publicly available test corpus, our hybrid features selection is able to achieve 94% accuracy rate.

Security in uncertain times: policies for increasing the popularity of life annuities among retirees

Life annuities offer retirees an assured income stream for as long as they live. This makes it surprising that they are unpopular in most markets where their purchase is not compelled by government policy. With the numbers of retirees in the population set to increase dramatically, this low take-up rate of life annuities could exacerbate financial insecurity. Consequently, it is in society’s interest to implement non-coercive policies that increase annuitization levels. Although there is research that has focused on the possible causes of low annuitization rates, much of this research falls short of suggesting comprehensive strategies for persuading retirees to annuitize their savings.