Whole grains and health:attitudes to whole grains against a prevailing background of increased marketing and promotion

To explore current awareness and perceptions of whole grain foods and perceived barriers and facilitators of whole grain consumption.

Digital loyalty card 'Big Data' and small business marketing: Formal versus informal or complementary?

This article proposes that a complementary relationship exists between the formalised nature of digital loyalty card data, and the informal nature of small business market orientation. A longitudinal, case-based research approach analysed this relationship in small firms given access to Tesco Clubcard data. The findings reveal a new-found structure and precision in small firm marketing planning from data exposure; this complemented rather than conflicted with an intuitive feel for markets. In addition, small firm owners were encouraged to include employees in marketing planning.

Relaunching Titanic: Memory and Marketing in the New Belfast

Relaunching Titanic critically considers the invocation of Titanic heritage in Belfast in contributing to a new ‘post-conflict’ understanding of the city. The authors address how the memory of Titanic is being and should be represented in the place of its origin, from where it was launched into the collective consciousness and unconscious of western civilization.

Relaunching Titanic examines the issues in the context of international debates on the tension between place marketing of cities and other alternative portrayals of memory and meaning in places. Key questions include the extent to which the goals of economic development are congruous with the ‘contemplative city’ and especially the need for mature and creative reflection in the ‘post-conflict’ city, whether development interests have taken precedence over the need for a deeper appreciation of a more nuanced Titanic legacy in the city of Belfast, and what Belfast shares with other places in considering the sacred and profane in memory construction.

While Relaunching Titanic focuses on the conflicted history of Belfast and the Titanic, it will have lessons for planners and scholars of city branding, tourism, and urban re-imaging.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Bricks vs. clicks: Which is better for marketing remanufactured products?

The economical and environmental benefits are the central issues for remanufacturing. Whereas extant remanufacturing research focuses primarily on such issues in remanufacturing technologies, production planning, inventory control and competitive strategies, we provide an alternative yet somewhat complementary approach to consider both issues related to different channels structures for marketing remanufactured products. Specifically, based on observations from current practice, we consider a manufacturer sells new units through an independent retailer but with two options for marketing remanufactured products: (1) marketing through its own e-channel (Model M) or (2) subcontracting the marketing activity to a third party (Model 3P). A central result we obtain is that although Model M is always greener than Model 3P, firms have less incentive to adopt it because both the manufacturer and retailer may be worse off when the manufacturer sells remanufactured products through its own e-channel rather than subcontracting to a third party. Extending both models to cases in which the manufacturer interacts with multiple retailers further reveals that the more retailers in the market, the greener Model M relative to Model 3P.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.