Making Social Work Education App’ier : The Process of Developing Information-based Apps for Social Work Education and Practice

Mobile App technology in social work education remains in the embryonic stages of development with a few notable exceptions. The use of Apps in College and University settings has been reported in other sectors of higher education, although there is a paucity of research in relation to its relevance to social work education and practice. The following article describes the creation of four social work education and practice Apps by a team of social work educators. The primary focus is on the design process and the partnership approach to the creation of the tools. It also outlines the rationale for the App development, the working process and the theoretical framework underpinning mobile learning. Furthermore, it provides information on the level of usage of the Apps according to geographical location, download information and time spent on each section of the App. The article also incorporates a pragmatic summary of developmental guidelines which may aid social work educators in the development and implementation of specialist information-based Apps for education and practice.

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model (PBSM) for Android does not address this threat, as it is rather limited to mitigating risks due to individual apps. This paper presents a technique for assessing the threat of collusion for apps, which is a first step towards quantifying collusion risk, and allows us to narrow down to candidate apps for collusion, which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29000 Android apps provided by Intel Security.

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.

Knowledge translation in men’s health research: development and delivery of content for use online

Background: Men can be hard to reach with face-to-face health-related information, while increasingly, research shows that they are seeking health information from online sources. Recognizing this trend, there is merit in developing innovative online knowledge translation (KT) strategies capable of translating research on men’s health into engaging health promotion materials. While the concept of KT has become a new mantra for researchers wishing to bridge the gap between research evidence and improved health outcomes, little is written about the process, necessary skills, and best practices by which researchers can develop online knowledge translation.
Objective: Our aim was to illustrate some of the processes and challenges involved in, and potential value of, developing research knowledge online to promote men’s health.

Methods: We present experiences of KT across two case studies of men’s health. First, we describe a study that uses interactive Web apps to translate knowledge relating to Canadian men’s depression. Through a range of mechanisms, study findings were repackaged with the explicit aim of raising awareness and reducing the stigma associated with men’s depression and/or help-seeking. Second, we describe an educational resource for teenage men about unintended pregnancy, developed for delivery in the formal Relationship and Sexuality Education school curricula of Ireland, Northern Ireland (United Kingdom), and South Australia. The intervention is based around a Web-based interactive film drama entitled “If I Were Jack”.

Results: For each case study, we describe the KT process and strategies that aided development of credible and well-received online content focused on men’s health promotion. In both case studies, the original research generated the inspiration for the interactive online content and the core development strategy was working with a multidisciplinary team to develop this material through arts-based approaches. In both cases also, there is an acknowledgment of the need for gender and culturally sensitive information. Both aimed to engage men by disrupting stereotypes about men, while simultaneously addressing men through authentic voices and faces. Finally, in both case studies we draw attention to the need to think beyond placement of content online to delivery to target audiences from the outset.

Conclusions: The case studies highlight some of the new skills required by academics in the emerging paradigm of translational research and contribute to the nascent literature on KT. Our approach to online KT was to go beyond dissemination and diffusion to actively repackage research knowledge through arts-based approaches (videos and film scripts) as health promotion tools, with optimal appeal, to target male audiences. Our findings highlight the importance of developing a multidisciplinary team to inform the design of content, the importance of adaptation to context, both in terms of the national implementation context and consideration of gender-specific needs, and an integrated implementation and evaluation framework in all KT work.

Development of an app to support learning in A-level maths

This project involved producing an app for smart devices to enable modernised learning for A-level maths students. Research in a stakeholder school showed that 94% of pupils surveyed within the upper-secondary level owned a smartphone and most owned a tablet also, emphasising the opportunity for using apps to support learning. The app was developed using iBuildApp, an online app-creation programme which requires no programming. Past exam questions and solutions, notes and video tutorials were included and the topic was vectors, identified by teachers as problematic. Pupils generally found the app easy to use and wanted further development. The videos were popular despite this not ranking highly as a preferred method of revision previously. Teachers were happy for pupils to use the app to supplement their learning, both in the classroom and outside.

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.

Belfast Soundwalks

The Belfast Soundwalks project, led by Professor Pedro Rebelo and co-ordinated by Dr Sarah Bass (Sonic Arts Research Centre) in collaboration with Belfast City Council (BCC), aims to use sonic art to engage the public through the development of a locative mobile phone app. Targeting both tourists and citizens of the city, this project aims to sonically enhance the experience of a number of areas of the city, including destinations that may not traditionally be accessed as attractions by visitors and/or disregarded or undervalued by local residents. The project will bring together a number of sonic artists/composers who will create approximately ten soundwalks around the city, while liaising with BCC to distribute the resulting app to the public in line with their tourism and cultural strategy. The project is centred on the development of smart phone apps which provide unique listening experiences associated with key places in the city. The user’s location in the city is tracked through GPS which triggers sound materials ranging from speech to environmental sound and abstract imagined sound worlds. Additionally, local community groups will be consulted in order to evaluate and reflect upon the effectiveness of the soundwalks.

The project builds on the success of the Literary Belfast app and aims to further strengthen links between Queen’s University Belfast and Belfast City Council through facilitating the dissemination of an art form not widely experienced by the general public. Through the newly created Institute for Collaborative Research in the Humanities, directed by Professor John Thompson we are articulating this project with Queen’s consortium partners, Newcastle University and Durham University.

“The Arts and Humanities Research Council (AHRC) funds world-class, independent researchers in a wide range of subjects: ancient history, modern dance, archaeology, digital content, philosophy, English literature, design, the creative and performing arts, and much more. This financial year the AHRC will spend approximately £98m to fund research and postgraduate training in collaboration with a number of partners. The quality and range of research supported by this investment of public funds not only provides social and cultural benefits but also contributes to the economic success of the UK. For further information on the AHRC, please go to: www.ahrc.ac.uk”.

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.