A criminological analysis : using real-time monitoring to gather data on online predators

The Internet presents a constantly evolving frontier for criminology and policing, especially in relation to online predators – paedophiles operating within the Internet for safer access to children, child pornography and networking opportunities with other online predators. The goals of this qualitative study are to undertake behavioural research – identify personality types and archetypes of online predators and compare and contrast them with behavioural profiles and other psychological research on offline paedophiles and sex offenders. It is also an endeavour to gather intelligence on the technological utilisation of online predators and conduct observational research on the social structures of online predator communities. These goals were achieved through the covert monitoring and logging of public activity within four Internet Relay Chat(rooms) (IRC) themed around child sexual abuse and which were located on the Undernet network. Five days of monitoring was conducted on these four chatrooms between Wednesday 1 to Sunday 5 April 2009; this raw data was collated and analysed. The analysis identified four personality types – the gentleman predator, the sadist, the businessman and the pretender – and eight archetypes consisting of the groomers, dealers, negotiators, roleplayers, networkers, chat requestors, posters and travellers. The characteristics and traits of these personality types and archetypes, which were extracted from the literature dealing with offline paedophiles and sex offenders, are detailed and contrasted against the online sexual predators identified within the chatrooms, revealing many similarities and interesting differences particularly with the businessman and pretender personality types. These personality types and archetypes were illustrated by selecting users who displayed the appropriate characteristics and tracking them through the four chatrooms, revealing intelligence data on the use of proxies servers – especially via the Tor software – and other security strategies such as Undernet’s host masking service. Name and age changes, which is used as a potential sexual grooming tactic was also revealed through the use of Analyst’s Notebook software and information on ISP information revealed the likelihood that many online predators were not using any safety mechanism and relying on the anonymity of the Internet. The activities of these online predators were analysed, especially in regards to child sexual grooming and the ‘posting’ of child pornography, which revealed a few of the methods in which online predators utilised new Internet technologies to sexually groom and abuse children – using technologies such as instant messengers, webcams and microphones – as well as store and disseminate illegal materials on image sharing websites and peer-to-peer software such as Gigatribe. Analysis of the social structures of the chatrooms was also carried out and the community functions and characteristics of each chatroom explored. The findings of this research have indicated several opportunities for further research. As a result of this research, recommendations are given on policy, prevention and response strategies with regards to online predators.

Real-time monitoring of nucleation-growth cycle of carbon nanoparticles in acetylene plasmas

Quantum cascade laserabsorption spectroscopy was used to measure the absolute concentration of acetylene in situ during the nanoparticle growth in Ar + C2H2 RF plasmas. It is demonstrated that the nanoparticle growth exhibits a periodical behavior, with the growth cycle period strongly dependent on the initial acetylene concentration in the chamber. Being 300 s at 7.5% of acetylene in the gas mixture, the growth cycle period decreases with the acetylene concentration increasing; the growth eventually disappears when the acetylene concentration exceeds 32%. During the nanoparticle growth, the acetylene concentration is small and does not exceed 4.2% at radio frequency (RF) power of 4 W, and 0.5% at RF power of 20 W. An injection of a single acetylene pulse into the discharge also results in the nanoparticlenucleation and growth. The absorption spectroscopy technique was found to be very effective for the time-resolved measurement of the hydrocarbon content in nanoparticle-generatingplasmas.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.

Real time detectionof airborne fungal spores and investigations into their dynamics in indoor air

Concern regarding the health effects of indoor air quality has grown in recent years, due to the increased prevalence of many diseases, as well as the fact that many people now spend most of their time indoors. While numerous studies have reported on the dynamics of aerosols indoors, the dynamics of bioaerosols in indoor environments are still poorly understood and very few studies have focused on fungal spore dynamics in indoor environments. Consequently, this work investigated the dynamics of fungal spores in indoor air, including fungal spore release and deposition, as well as investigating the mechanisms involved in the fungal spore fragmentation process. In relation to the investigation of fungal spore dynamics, it was found that the deposition rates of the bioaerosols (fungal propagules) were in the same range as the deposition rates of nonbiological particles and that they were a function of their aerodynamic diameters. It was also found that fungal particle deposition rates increased with increasing ventilation rates. These results (which are reported for the first time) are important for developing an understanding of the dynamics of fungal spores in the air. In relation to the process of fungal spore fragmentation, important information was generated concerning the airborne dynamics of the spores, as well as the part/s of the fungi which undergo fragmentation. The results obtained from these investigations into the dynamics of fungal propagules in indoor air significantly advance knowledge about the fate of fungal propagules in indoor air, as well as their deposition in the respiratory tract. The need to develop an advanced, real-time method for monitoring bioaerosols has become increasingly important in recent years, particularly as a result of the increased threat from biological weapons and bioterrorism. However, to date, the Ultraviolet Aerodynamic Particle Sizer (UVAPS, Model 3312, TSI, St Paul, MN) is the only commercially available instrument capable of monitoring and measuring viable airborne micro-organisms in real-time. Therefore (for the first time), this work also investigated the ability of the UVAPS to measure and characterise fungal spores in indoor air. The UVAPS was found to be sufficiently sensitive for detecting and measuring fungal propagules. Based on fungal spore size distributions, together with fluorescent percentages and intensities, it was also found to be capable of discriminating between two fungal spore species, under controlled laboratory conditions. In the field, however, it would not be possible to use the UVAPS to differentiate between different fungal spore species because the different micro-organisms present in the air may not only vary in age, but may have also been subjected to different environmental conditions. In addition, while the real-time UVAPS was found to be a good tool for the investigation of fungal particles under controlled conditions, it was not found to be selective for bioaerosols only (as per design specifications). In conclusion, the UVAPS is not recommended for use in the direct measurement of airborne viable bioaerosols in the field, including fungal particles, and further investigations into the nature of the micro-organisms, the UVAPS itself and/or its use in conjunction with other conventional biosamplers, are necessary in order to obtain more realistic results. Overall, the results obtained from this work on airborne fungal particle dynamics will contribute towards improving the detection capabilities of the UVAPS, so that it is capable of selectively monitoring and measuring bioaerosols, for which it was originally designed. This work will assist in finding and/or improving other technologies capable of the real-time monitoring of bioaerosols. The knowledge obtained from this work will also be of benefit in various other bioaerosol applications, such as understanding the transport of bioaerosols indoors.

Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation

Background Despite its efficacy and cost-effectiveness, exercise-based cardiac rehabilitation is undertaken by less than one-third of clinically eligible cardiac patients in every country for which data is available. Reasons for non-participation include the unavailability of hospital-based rehabilitation programs, or excessive travel time and distance. For this reason, there have been calls for the development of more flexible alternatives. Methodology and Principal Findings We developed a system to enable walking-based cardiac rehabilitation in which the patient's single-lead ECG, heart rate, GPS-based speed and location are transmitted by a programmed smartphone to a secure server for real-time monitoring by a qualified exercise scientist. The feasibility of this approach was evaluated in 134 remotely-monitored exercise assessment and exercise sessions in cardiac patients unable to undertake hospital-based rehabilitation. Completion rates, rates of technical problems, detection of ECG changes, pre- and post-intervention six minute walk test (6 MWT), cardiac depression and Quality of Life (QOL) were key measures. The system was rated as easy and quick to use. It allowed participants to complete six weeks of exercise-based rehabilitation near their homes, worksites, or when travelling. The majority of sessions were completed without any technical problems, although periodic signal loss in areas of poor coverage was an occasional limitation. Several exercise and post-exercise ECG changes were detected. Participants showed improvements comparable to those reported for hospital-based programs, walking significantly further on the post-intervention 6 MWT, 637 m (95% CI: 565–726), than on the pre-test, 524 m (95% CI: 420–655), and reporting significantly reduced levels of cardiac depression and significantly improved physical health-related QOL. Conclusions and Significance The system provided a feasible and very flexible alternative form of supervised cardiac rehabilitation for those unable to access hospital-based programs, with the potential to address a well-recognised deficiency in health care provision in many countries. Future research should assess its longer-term efficacy, cost-effectiveness and safety in larger samples representing the spectrum of cardiac morbidity and severity.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Integrating real time positioning systems to improve blind lifting and loading crane operations

Mobile/tower cranes are the most essential forms of construction plant in use in the construction industry but are also the subject of several safety issues. Of these, blind lifting has been found to be one of the most hazardous of crane operations. To improve the situation, a real-time monitoring system that integrates the use of a Global Positioning System (GPS) and Radio Frequency Identification (RFID) is developed. This system aims to identify unauthorized work or entrance of personnel within a pre-defined risk zone by obtaining positioning data of both site workers and the crane. The system alerts to the presence of unauthorized workers within a risk zone——currently defined as 3m from the crane. When this happens, the system suspends the power of the crane and a warning signal is generated to the safety management team. In this way the system assists the safety management team to manage the safety of hundreds of workers simultaneously. An onsite trial with debriefing interviews is presented to illustrate and validate the system in use.

Real-time price based home energy management scheduler

With the recent development of advanced metering infrastructure, real-time pricing (RTP) scheme is anticipated to be introduced in future retail electricity market. This paper proposes an algorithm for a home energy management scheduler (HEMS) to reduce the cost of energy consumption using RTP. The proposed algorithm works in three subsequent phases namely real-time monitoring (RTM), stochastic scheduling (STS) and real-time control (RTC). In RTM phase, characteristics of available controllable appliances are monitored in real-time and stored in HEMS. In STS phase, HEMS computes an optimal policy using stochastic dynamic programming (SDP) to select a set of appliances to be controlled with an objective of the total cost of energy consumption in a house. Finally, in RTC phase, HEMS initiates the control of the selected appliances. The proposed HEMS is unique as it intrinsically considers uncertainties in RTP and power consumption pattern of various appliances. In RTM phase, appliances are categorized according to their characteristics to ease the control process, thereby minimizing the number of control commands issued by HEMS. Simulation results validate the proposed method for HEMS.

Chirp-spread-spectrum-based real time location system for construction safety management: A case study

Observing the working procedure of construction workers is an effective means of maintaining the safety performance of a construction project. It is also difficult to achieve due to a high worker-to-safety-officer ratio. There is an imminent need for the development of a tool to assist in the real-time monitoring of workers, in order to reduce the number of construction accidents. The development and application of a real time locating system (RTLS) based on the Chirp Spread Spectrum (CSS) technique is described in this paper for tracking the real-time position of workers on construction sites. Experiments and tests were carried out both on- and off-site to verify the accuracy of static and dynamic targets by the system, indicating an average error of within one metre. Experiments were also carried out to verify the ability of the system to identify workers’ unsafe behaviours. Wireless data transfer was used to simplify the deployment of the system. The system was deployed in a public residential construction project and proved to be quick and simple to use. The cost of the developed system is also reported to be reasonable (around 1800USD) in this study and is much cheaper than the cost of other RTLS. In addition, the CCS technique is shown to provide an economical solution with reasonable accuracy compared with other positioning systems, such as ultra wideband. The study verifies the potential of the CCS technique to provide an effective and economical aid in the improvement of safety management in the construction industry.

Monitoring changes of the tibialis anterior during dorsiflexion with electromyography, sonomyography, dynamometry and kinematic signals

Dorsiflexion (DF) of the foot plays an essential role in both controlling balance and human gait. Electromyography and Sonomyography can provide information on several aspects of muscle function. The aim was to describe a new method for real-time monitoring of muscular activity, as measured using EMG, muscular architecture, as measured using SMG, force, as measured using dynamometry, and kinematic parameters, as measured using IS during isometric and isotonic contractions of the foot DF. The present methodology may be clinically relevant because it involves a reproducible procedure which allows the function and structure of the foot DF to be monitored.

Using honeypots to analyse anomalous Internet activities

Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.

A portable approach for the surveillance of dengue virus-infected mosquitoes

Dengue virus is the most significant human viral pathogen spread by the bite of an infected mosquito. With no vaccine or antiviral therapy currently available, disease prevention relies largely on surveillance and mosquito control. Preventing the onset of dengue outbreaks and effective vector management would be considerably enhanced through surveillance of dengue virus prevalence in natural mosquito populations. However, current approaches to the identification of virus in field-caught mosquitoes require relatively slow and labor intensive techniques such as virus isolation or RT-PCR involving specialized facilities and personnel. A rapid and portable method for detecting dengue virus-infected mosquitoes is described. Using a hand held battery operated homogenizer and a dengue diagnostic rapid strip the viral protein NS1 was detected as a marker of dengue virus infection. This method could be performed in less than 30 min in the field, requiring no downstream processing, and is able to detect a single infected mosquito in a pool of at least 50 uninfected mosquitoes. The method described in this study allows rapid, real-time monitoring of dengue virus presence in mosquito populations and could be a useful addition to effective monitoring and vector control responses.

Exercise-based cardiac rehabilitation for the 21st century

Exercise-based cardiac rehabilitation (CR) is efficacious in reducing mortality and hospital admissions; however it remains inaccessible to large proportions of the patient population. Removal of attendance barriers for hospital or centre-based CR has seen the promotion of home-based CR. Delivery of safe and appropriately prescribed exercise in the home was first documented 25 years ago, with the utilisation of fixed land-line telecommunications to monitor ECG. The advent of miniature ECG sensors, in conjunction with smartphones, now enables CR to be delivered with greater flexibility with regard to location, time and format, while retaining the capacity for real-time patient monitoring. A range of new systems allow other signals including speed, location, pulse oximetry, and respiration to be monitored and these may have application in CR. There is compelling evidence that telemonitored-based CR is an effective alternative to traditional CR practice. The long-standing barrier of access to centre-based CR, combined with new delivery platforms, raises the question of when telemonitored-based CR could replace conventional approaches as the standard practice.

A Hybrid Queue-based Bayesian Network Framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and Exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an $R^2$ goodness of fit of 0.9994 and 0.9982 respectively over a 10 hour test period. The utility of the framework is demonstrated on a number of usage scenarios including real time monitoring and `what-if' analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.