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Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases.

In the twenty-first century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks with automated decision making and sophisticated technology, no doubt we feel much more secure than half a century ago. As the technological advancement takes root, so does the maturity of security threats. It is common that today's critical infrastructures are operated by non-computer experts, e.g. nurses in health care, soldiers in military or firefighters in emergency services. In such challenging applications, protecting against insider attacks is often neither feasible nor economically possible, but these threats can be managed using suitable risk management strategies. Security technologies, e.g. firewalls, help protect data assets and computer systems against unauthorised entry. However, one area which is often largely ignored is the human factor of system security. Through social engineering techniques, malicious attackers are able to breach organisational security via people interactions. This paper presents a security awareness training framework, which can be used to train operators of critical infrastructure, on various social engineering security threats such as spear phishing, baiting, pretexting, among others.

Acknowledgments: This work was carried out during the studying duration of the first author for an MSc of AI degree at the University of Aberdeen, which was fully funded by Chevening Scholarships, the UK government's global scholarship programme, funded by the Foreign and Commonwealth Office (FCO) and partner organisations. We also thank Jette Viethen and other people at Arria NLG for their suggestions ; Peer reviewed ; Publisher PDF

This paper presents a review of user expectation towards Augmented Reality (AR) and the acceptance of AR for technologyenhanced teaching and learning. Augmented Reality is a technology that superimposes a computer-generated image over a user's view of the real world, thus providing a composite view. This technology has been used in many fields such as marketing, military, entertainment and many other sectors. Studies have found that AR technology can enhance teaching and learning, however more research still needs to be conducted about the acceptance of AR as a learning tool and what users in education expect from the technology. An understanding of the user expectation is one of the key foundations towards establishing better -designed AR systems and applications that will result in more acceptance of this technology. To help with this, this paper reviews previous research on user expectations of AR in education and its acceptance. © 2017 Che Samihah Che Dalim, Hoshang Kolivand, Faris A. Abuhashish, Mohd Shahrizal Sunar and Mark Billinghurst.

The Phillips Machine, or Monetary National Income Analogue Computer (MONIAC), is a hydraulic representation of cash flow within the UK economy in the early 20th century. It represents the circular flow of income (New Zealand Institute of Economic Research, 2015), shown by the economic equation Aggregate Demand = Consumer Expenditure + Investment + Government Spending + (Exports – Imports), or Y = C + I + G + (X – M), which is an important equation in determining the national output of an economy (Pettinger, 2008). Though no longer in use, of the 14 that were built, the majority were originally restricted to military and government use owing to their effectiveness in determining economic policy. This paper describes an implementation of an emulation of this machine that satisfies, or mostly satisfies, almost all of the requirements specified in the ISO/IEC 25010 software quality assurance standard and would be suitable for deployment to support A-Level Economics teaching. With further work it would also become suitable as part of a museum display of a Phillips Machine.

Emergency departments open 24 hours every day and may receive patients at any time most unexpectedly.Adequate resource planning to meet the various demands in emergency departments is arguably amongst the most critical challenges faced by hospital administrators due to the complexity of the system and diverse patient flows.In this paper, we present a computer simulation model to evaluate resource utilization among personnel and physical resources in a typical emergency department (ED) of a government hospital in Malaysia.The model allows administrators to see patient movement flows as well as how these flows are affected by resource capacity level in the ED.The use of this simulation model helps to evaluate ED operations, provides useful insight for possible areas of improvement, and directs the allocation of specific resources for maximal impact. Three scenarios were tested to find out the impact of patient surge on ED performance measures. Results from the scenarios show that a 30% increase of attendances according to triage zones affects most of the performance measures.In addition, medical assistants are the critical resources with average utilization greater than 70%.

There are no doubts that the Internet-of-Things (IoT) has conquered the ICT industry to the extent that many governments and organizations are already rolling out many anywhere,anytime online services that IoT sustains. However, like any emerging and disruptive technology, multiple obstacles are slowing down IoT practical adoption including the passive nature and privacy invasion of things. This paper examines how to empower things with necessary capabilities that would make them proactive and responsive. This means things can, for instance reach out to collaborative peers, (un)form dynamic communities when necessary, avoid malicious peers, and be "questioned" for their actions. To achieve such empowerment, this paper presents an approach for agentifying things using norms along with commitments that operationalize these norms. Both norms and commitments are specialized into social (i.e., application independent) and business (i.e., application dependent), respectively. Being proactive, things could violate commitments at run-time, which needs to be detected through monitoring. In this paper, thing agentification is illustrated with a case study about missing children and demonstrated with a testbed that uses di_erent IoT-related technologies such as Eclipse Mosquitto broker and Message Queuing Telemetry Transport protocol. Some experiments conducted upon this testbed are also discussed.

Fitness improving innovations occur in populations of organisms as genetic changes (mutations) that allow better fit with the environmental niche of the organisms. Similarly, fitness improving innovations may occur in the context of human communities as well in terms of socio-economic innovations (e.g. new ways of organizing the military, new products or services) that lead to more efficient use of available resources. Here we explore the link between such innovations and the harshness of the environment, where the populations live. Environmental harshness characterizes the availability of population growth supporting resources in the environment. Our analysis shows that if the harshness of the environment varies smoothly with the distance, the expected extent of fitness improving innovations and of the resource utilization efficiency of populations depends in a combined linear and harmonic manner on the harshness of the environment at the location of origin of the populations. We explore the implications of this result for particular cases of both biological and social environments (e.g. gene drives, business innovation).

Nowadays, cartography, otherwise known as mapping, are serving people in multiple aspects of livelihood and administration like planning, rescue, military, and tourism. The information is usually presented in the map by using legends, elevation, and contour.With the passage of time, many new methods of mapping are introduced which are divided into the non-digital and digital version.Another method used is the incorporation of 3D modelling in virtual aspect by simulating real-world data.In the recent years using the game engine is one of the important 3D modelling approaches obtained.The game engine has a lot more capabilities to simulate real-world terrains whereas using database support for terrain visualisation, new functionality can be used. This article discussed how to utilise game engine technology for developing 3D terrain visualisation with oil palm tree data.With the success of the system, it, therefore, gives benefits to potential visitors especially the manager of oil palm plantation, decision makers and planners to explore the online 3D terrain visualisation with oil palm tree data and manage it.

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.

Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle (e.g., cars) have numbers with dark background, while public vehicle (taxis/cabs) have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks namely, BLSTM (Bi-Directional Long Short Term Memory), for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting (DCV), which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.

The study on citizens' behaviors and thinking is often dependent on the citizens' various levels of education, cultures, nature of jobs, experiences, and environments. These characteristics form the basis of Citizens' Self-Knowledge. Decision making in e-government is a collaborative action between the government who functions to serve the public and the citizen who are the recipient of the government services. Such collaboration would provide best e-government model (planning, application) and other parts of the e- government initiatives. This study is intended to improve the model of decision-making in E-government using the Citizens' self-knowledge. This article is intended to discuss the results and findings from the pilot study to develop the decision making model in Iraqi e-government by employing citizens' self-knowledge characteristics in decision making.

Wireless sensor network (WSN) has garnered remarkable attention due to its wide supports for plenty of applications such as, health systems; military based applications, environmental monitoring, and tactical system. In Contention Based Medium Access Control (MAC) protocols related to the energy consumption. In this paper, a combative review of energy consumption in Contention-Based MAC protocols was provided. Furthermore, a general comparison that stated the strengths and drawbacks with every utilized technique was offered. The main aim of this paper is to assist the researcher to choose the right protocol for developing purpose or further investigation regarding the performance.