AßPP over-expressing transgenic rat model of Alzheimer's disease utilizing the Tg2576 mouse protocol.

The current study examined behavioral and histological effects of amyloid-ß (Aß) protein precursor (AßPP) overexpression in transgenic (Tg) rats created using the same gene, mutation, and promoter as the Tg2576 mouse model of Alzheimer's disease (AD). Male Tg+ rats were bred with female wild-type rats to generate litters of hemizygous Tg+ and Tg- offspring. Tg+ rats and Tg- littermates were tested for memory deficits at 4, 8, and 12 months old using a water-maze procedure. There were no significant behavioral differences between Tg+ rats and Tg- littermates at 4 months old but there were significant differences at 8 and 12 months old, and in probe trials at 8 and 12 months old, the Tg+ rats spent significantly less time and covered less distance in the platform zone. Under acquisition of a fixed-consecutive number schedule at 3 months old, Tg- littermates demonstrated a longer latency to learning the response rule than Tg+ rats; while this might seem paradoxical, it is consistent with the role of overexpression of AßPP in learning. Histological analyses revealed activated astrocytes in brains of Tg+ rats but not Tg- littermates at 6 months old, and thioflavin-S positive staining in the hippocampus and cortex of 17-month old Tg+ rats but not Tg- littermates. Quantification of Aß load in the brain at 22 months indicated high levels of Aß38, Aß40, and Aß42 in the Tg+ rats. These data suggest this model might provide a valuable resource for AD research.

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.

Belfast Soundwalks: Experiences in Sound and Place through Locative Media

This article outlines the ongoing development of a locative smartphone app for iPhone and Android phones entitled The Belfast Soundwalks Project. Drawing upon a method known as soundwalking, the aim of this app is to engage the public in sonic art through the creation of up to ten soundwalks within the city of Belfast. This paper discusses the use of GPS enabled mobile devices in the creation of soundwalks in other cities. The authors identify various strategies for articulating an experience of listening in place as mediated by mobile technologies. The project aims to provide a platform for multiple artists to develop site-specific sound works which highlight the relationship between sound, place and community. The development of the app and the app interface are discussed, as are the methods employed to test and evaluate the project.

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.

Indoor and Outdoor monitoring of photocatalytic activity using a mobile phone app. and a photocatalytic activity indicator ink (paii)

A resazurin (Rz) based photocatalyst activity indicator ink (paii) is used to test the activity of commercial self-cleaning materials. The semiconductor photocatalyst driven colour change of the ink is monitored indoors and outside using a simple mobile phone application that measures the RGB colour components of the digital image of the paii-covered, irradiated sample in real time. The results correlate directly with those generated using a traditional, lab-bound method of analysis (UV–vis spectrophotometry).

Vision and deaf awareness training is it app.ropriate?

Title
Visual and deaf awareness training is it app.ropriate
Purpose
Some of our most vulnerable patients have a sensory deficit. An app which focused on patients with a vision and/or hearing loss was developed for healthcare students. The intent was to embed the core values necessary for students to provide appropriate care for patients with a sensory deficit.
Setting
Queen’s University Belfast, School of Nursing and Midwifery.
Methods
Stage 1
A review of current sensory awareness training in the United Kingdom
Stage 2
Application for funding
Stage 3
Development of a teaching tool template with the essential aspects required for sensory awareness training
Stage 4
Collaboration with others: Royal National Institute for the Blind, Action on Hearing Loss, Computer technician.
Stage 5
Production and transfer of multimedia outputs onto a software application system.
Stage 6
App Piloted with a sample of lecturers (n=5), undergraduate nursing students (n=20), service users (n=5)
Stage 7
Editing
Stage 8
App made available to all undergraduate nursing students
Stage 9
App evaluation (n=300)



Results
Overall nursing students positively evaluated the app, 100% of students rated the app between good and excellent. Qualitative evidence from service users and practice partnerships was extremely positive:
"At last I feel listened too in respect to my hearing loss and empowered. I don't feel like I am complaining I am actually helping to create something which should benefit staff and all of us with a hearing or vision loss". Patient
“Very insightful into the lives of those with a disability will be so useful in practice as an aid to jog my memory". 1st year nursing student
Conclusion
It is hoped that further evaluation and implementation of the app will show an improved quality to the care delivered to those with a sensory deficit. We believe that by working in partnership with service users we have helped to create an innovative tool that benefits both staff and patients.
Financial disclosure Yes
Funding of £2700 was awarded in 2014 through the Martha McMenamin Memorial Northern Ireland Scholarship.

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.

An evaluation of the Socrative (personal response system) app for increasing student engagement and learning in an undergraduate psychology curriculum.

Personal response systems using hardware such as 'clickers' have been around for some time, however their use is often restricted to multiple choice questions (MCQs) and they are therefore used as a summative assessment tool for the individual student. More recent innovations such as 'Socrative' have removed the need for specialist hardware, instead utilising web-based technology and devices common to students, such as smartphones, tablets and laptops. While improving the potential for use in larger classrooms, this also creates the opportunity to pose more engaging open-response questions to students who can 'text in' their thoughts on questions posed in class. This poster will present two applications of the Socrative system in an undergraduate psychology curriculum which aimed to encourage interactive engagement with course content using real-time student responses and lecturer feedback. Data is currently being collected and result will be presented at the conference.
The first application used Socrative to pose MCQs at the end of two modules (a level one Statistics module and level two Individual Differences Psychology module, class size N≈100), with the intention of helping students assess their knowledge of the course. They were asked to rate their self-perceived knowledge of the course on a five-point Likert scale before and after completing the MCQs, as well as their views on the value of the revision session and any issues that had with using the app. The online MCQs remained open between the lecture and the exam, allowing students to revisit the questions at any time during their revision.
This poster will present data regarding the usefulness of the revision MCQs, the metacognitive effect of the MCQs on student's judgements of learning (pre vs post MCQ testing), as well as student engagement with the MCQs between the revision session and the examination. Student opinions on the use of the Socrative system in class will also be discussed.
The second application used Socrative to facilitate a flipped classroom lecture on a level two 'Conceptual Issues in Psychology' module, class size N≈100). The content of this module requires students to think critically about historical and contemporary conceptual issues in psychology and the philosophy of science. Students traditionally struggle with this module due to the emphasis on critical thinking skills, rather than simply the retention of concrete knowledge. To prepare students for the written examination, a flipped classroom lecture was held at the end of the semester. Students were asked to revise their knowledge of a particular area of Psychology by assigned reading, and were told that the flipped lecture would involve them thinking critically about the conceptual issues found in this area. They were informed that questions would be posed by the lecturer in class, and that they would be asked to post their thoughts using the Socrative app for a class discussion. The level of preparation students engaged in for the flipped lecture was measured, as well as qualitative opinions on the usefulness of the session. This poster will discuss the level of student engagement with the flipped lecture, both in terms of preparation for the lecture, and engagement with questions posed during the lecture, as well as the lecturer's experience in facilitating the flipped classroom using the Socrative platform.

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.