A UBIQUITOUS TECHNOLOGY FRAMEWORK FOR CURBING THE BOKO HARAM MENACE IN NIGERIA

The security threat posed by the Boko Haram sect in Nigeria has remained unabated with no hope in sight. Weexamined the concept and applications of ubiquitous technology and taking advantage of its potentials, proposed anarchitectural framework – N-tier Ubiquitous Architectural Framework (NUAF) – for curbing the Boko Harammenace in Nigeria. NUAF is a hierarchical and segregational distributed architectural framework which in itssimplest form consists of the base-tier, inner-tier and top-tier. The real life implementation of NUAF is howeverdependent on some anticipated research sucesses in nanotechnology and wireless network security.Keywords: Internal security, Terrorism, Boko Haram, Ubiquitous technology and Distributed architectur

Interactive Metal Fatigue; A critical lens for the assessment of socio-technical reconfigurations in traffic

Interactive metal fatigue (IMF) is an elegant re-appreciation of the concept of ‘interpassivity’, describing how it develops through minifractures in subjects’ attempts to keep up with societal demands for interactivity. Other than the original art-philosophical and psychoanalytical understandings, this rather historical conceptualization opens up the ‘interpassivity’ notion to sociological and political research. Particularly promising, it will be argued, is its aptitude to diagnose and articulate the often so elusive (side-) effects of socio-technical ‘system innovations’. Currently these tend to be evaluated in terms of ‘sustainability’, but this notion seems insufficient to capture the multi-sidedness of the reconfigurations involved. Socio-technical innovations are known to be contested social changes. Yet what is it that makes them contested? How can their societal relevance be appreciated? And considering that assessment in terms of ‘sustainability impacts’ leaves certain problematic aspects underexposed, how could the notion of ‘interactive metal fatigue’ enrich our understanding of socio-technical innovations

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time

Detecting the Early Drop of Attention using EEG Signal

The capability   to detect the drop of attention as early as possible has many practical applications including for the development of the early warning system for those who involve in high-risk works that  require a constant level of concentration. This study intends to  develop such the capability on the basis of the data of the brain   waves: delta, theta, alpha, beta, and gamma. For the purpose, a number of participants are asked  to participate in the study where their  brain waves are recorded by using a low-cost Neurosky Mindwave EEG sensor. In the process, the  participants are performing a continuous performance test from which their attention levels are directly measured in  the form of the response time in conjunction to those waves. When the response time is much longer than  a normal one, the participant attention is assumed  to be dropped. A simple k-NN classification method is used with the k = 3. The results are the following. The best detection of the attention drop is achieved when  the attention features are extracted   from the earliest stage of the brain wave signals. The brain wave signal should be  recorded longer than 1 s since the time the stimulus is presented as a short signal  leads to a poor categorization. A significant drop in the level of response time is required to provide the brain signal that better predicts the change of the attention

Differentiating Active And Passive Fatigue States With The Use Of Electroencephalography

With advances in automation technology, it is becoming essential to understand how automation affects human operators. A concern for the implementation of automation technology is the interactive effects it has with operator cognitive fatigue. Desmond and Hancock (2001) proposed that two types of fatigue can arise depending on the nature of the task: active and passive. Active fatigue results when operators must make constant perceptual-motor adjustments during high task demands, while passive fatigue results from operators executing little or no perceptual-motor adjustments during low task demands, similar to when automation is employed. The purpose of this study was to use electroencephalographic (EEG) indices of workload, engagement, and a candidate marker of strain under fatigue in conjunction with performance and subjective measures to differentiate active and passive fatigue states. Participants (N = 84) performed a generalized flight simulator for 62 min either under active, passive, or control conditions. Passive fatigue was characterized by reduced EEG engagement and initially elevated and stable ratios of Fz theta to POz alpha power compared to active fatigue. Subjective measure results indicated that passive fatigue was characterized by reduced ratings of alertness and workload compared to active fatigue. No performance differences were observed between fatigue conditions; however, an overall speed-accuracy trade-off was observed from pre to post fatigue induction. This study demonstrated that different fatigue states produce different effects on EEG indices. These results have potential applications for developing augmented cognition technologies that deliver appropriate fatigue countermeasures in automated operational environments

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour

In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dynamic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex monitoring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains. © 2011 - IOS Press and the authors. All rights reserved

Attention and Task Engagement During Automated Driving

Many young drivers suffer fatal crashes each year in the United States at a rate approximately three times greater than more experienced drivers. Automated driving systems may serve to mitigate young drivers high crash rates but remain underexplored in research. This dissertation project examined the effects of levels of automation and interestingness of auditory clips on latent hazard anticipation in young drivers during simulated driving. Participants drove a vehicle at varying levels of vehicle automation (SAE Level 0, 2, or 3) in simulated scenarios, each containing a latent hazard event during which a boring, neutral, or interesting auditory clip was played. After completing all scenarios, participants completed an auditory stimuli recognition test and a questionnaire measuring the drivers’ calibration of their LHA performance. Results demonstrated that those in the L3 condition anticipated significantly fewer hazards than those in the L0 condition, corroborating previous research (Samuels et al., 2020). However, those in the L3 condition were also significantly poorer at anticipating latent hazards than those in the L2 condition, suggesting the importance of instruction on a drivers’ attentional allocation policy. A tradeoff was found between latent hazard anticipation and auditory recognition scores indicating the allocation of limited attentional resources as predicted by the Yamani and Horrey (2018) model. Interestingness of auditory stimuli had little to no effect on latent hazard anticipation. In general, automation may improve the multitasking ability of a young driver piloting L2 automation, but this benefit is lost for drivers of L3 automation. Instead, young drivers piloting L3 automation may anticipate latent hazards at rates as low as those observed in newly licensed drivers, and may be completely unaware of their failure to anticipate such hazards. The current research illustrates the criticality of user guidance when handling automated driving systems and serves as one step towards understanding the complex relationship between human drivers and automated systems

Investigating Tafheet as a Unique Driving Style Behaviour

Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour