What I want for my Social Network privacy

Social Networks (SN) users have various privacy requirements to protect their information; to address this issue, a six-stage thematic analysis of scholarly articles related to SN user privacy concerns were synthesized. Then this research combines mixed methods research employing the strengths of quantitative and qualitative research to investigate general SN users, and thus construct a new set of ?ve primary and Twenty-?ve secondary SN user privacy requirements. Such an approach has been rarely used to examine the privacy requirements. Factor analysis results show superior agreement with theoretical predictions and signi?cant improvement over previous alternative models of SN user privacy requirements. This research presented here has the potential to provide for the development of more sophisticated privacy controls which will increase the ability of SN users to: specify their rights in SNs and to determine the protection of their own SN data.

Effective routing with utilisation of cross-layer information for industrial wireless sensor networks

This thesis focuses on providing reliable data transmissions in large-scale industrial wireless sensor networks through improving network layer protocols. It addresses three major problems: scalability, dynamic industrial environments and coexistence of multiple types of data traffic in a network. Theoretical developments are conducted, followed by simulation studies for verification of theoretic results. The approach proposed in this thesis has been shown to be effective for large-scale network implementation and to provide improved data transmission reliability for both periodic and sporadic traffic.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Enhanced fast handover triggering mechanism for fast proxy mobile IPv6

Handover performance is critical to support real-time traffic applications in wireless network communications. The longer the handover delay is, the longer an Mobile Node (MN) is prevented from sending and receiving any data packet. In real-time network communication applications, such as VoIP and video-conference, a long handover delay is often unacceptable. In order to achieve better handover performance, Fast Proxy Mobile IPv6 (FPMIPv6) has been standardised as an improvement to the original Proxy Mobile IPv6 (PMIPv6) in the Internet Engineering Task Force (IETF). The FPMIPv6 adopts a link layer triggering mechanism to perform two modes of operation: predictive and reactive modes. Using the link layer triggering, the handover performance of the FPMIPv6 can be improved in the predictive mode. However, an unsuccessful predictive handover operation will lead to activation of a reactive handover. In the reactive mode, MNs still experience long handover delays and a large amount of packet loss, which significantly degrade the handover performance of the FPMIPv6. Addressing this problem, this thesis presents an Enhanced Triggering Mechanism (ETM) in the FPMIPv6 to form an enhanced FPMIPv6 (eFPMIPv6). The ETM reduces the most time consuming processes in the reactive handover: the failed Handover Initiate (HO-Initiate) delay and bidirectional tunnel establishment delay. Consequently, the overall handover performance of the FPMIPv6 is enhanced in the eFPMIPv6. To show the advantages of the proposed eFPMIPv6, a theoretical analysis is carried out to mathematically model the performance of PMIPv6, FPMIPv6 and eFPMIPv6. Extensive case studies are conducted to validate the effectiveness of the presented eFPMIPv6 mechanism. They are carried out under various scenarios with changes in network link delay, traffic load, number of hops and MN moving velocity. The case studies show that the proposed mechanism ETM reduces the reactive handover delay, and the presented eFPMIPv6 outperforms the PMIPv6 and FPMIPv6 in terms of the overall handover performance.

Confucius Institutes and the network communication approach to public diplomacy

Discussions of public diplomacy in recent years have paid a growing amount of attention to networks. This network perspective is understood to provide insights into various issues of public diplomacy, such as its effects, credibility, reputation, identity and narratives. This paper applies the network idea to analyse China’s Confucius Institutes initiative. It understands Confucius Institutes as a global network and argues that this network structure has potential implications for the operation of public and cultural diplomacy that are perhaps underestimated in existing accounts of Chinese cultural diplomacy. In particular, it is noted that the specific setup of Confucius Institutes requires the engagement of local stakeholders, in a way that is less centralised and more networked than comparable cultural diplomacy institutions. At the same time, the development of a more networked for of public cultural diplomacy is challenged in practice by both practical issues and the configuration of China’s state-centric public diplomacy system informed by the political constitution of the Chinese state.

A case study of the Iranian national railway and its absolute capacity expansion using analytical models

Identifying railway capacity is an important task that can identify "in principal" whether the network can handle an intended traffic flow, and whether there is any free capacity left for additional train services. Capacity determination techniques can also be used to identify how best to improve an existing network, and at least cost. In this article an optimization approach has been applied to a case study of the Iran national railway, in order to identify its current capacity and to optimally expand it given a variety of technical conditions. This railway is very important in Iran and will be upgraded extensively in the coming years. Hence the conclusions in this article may help in that endeavor. A sensitivity analysis is recommended to evaluate a wider range of possible scenarios. Hence more useful lower and upper bounds can be provided for the performance of the system

Antisocial behaviour orders : Unanticipated directions in social network site development

Abstract: Social network technologies, as we know them today have become a popular feature of everyday life for many people. As their name suggests, their underlying premise is to enable people to connect with each other for a variety of purposes. These purposes however, are generally thought of in a positive fashion. Based on a multi-method study of two online environments, Habbo Hotel and Second Life, which incorporate social networking functionality, we she light on forms of what can be conceptualized as antisocial behaviours and the rationales for these. Such behaviours included: scamming, racist/homophobic attacks, sim attacks, avatar attacks, non-conformance to contextual norms, counterfeiting and unneighbourly behaviour. The rationales for sub behaviours included: profit, fun, status building, network disruption, accidental acts and prejudice. Through our analysis we are able to comment upon the difficulties of defining antisocial behaviour in such environments, particularly when such environments are subject to interpretation vis their use and expected norms. We also point to the problems we face in conducting our public and private lives given the role ICTs are playing in the convergence of these two spaces and also the convergence of ICTs themselves.

Pipeline renewal decision support : a case study using LinEAR in a water pipeline network

This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

Proactive safety assessment in roadwork zones : a synthesis of surrogate measures of safety

Work zone safety studies have traditionally relied on historical crash records—an approach which is reactive in nature as it requires crashes to accumulate first before taking any preventive actions. However, detailed and accurate data on work zone crashes are often not available, as is the case for Australian road work zones. The lack of reliable safety records and the reactive nature of the crash-based safety analysis approach motivated this research to seek alternative and proactive measures of safety. Various surrogate measures of safety have been developed in the traffic safety literature including time to collision, time to accident, gap time, post encroachment time, required deceleration rate, proportion of stopping distances, lateral distance to departure, and time to departure. These measures express how close road-user(s) are from a potential crash by analysing their movement trajectories. A review of this fast-growing literature is presented in this paper from the viewpoint of applying the measures to untangle work zone safety issues. The review revealed that the use of the surrogate measures is very limited for analysing work zone safety, although numerous studies have used these measures for analysing safety in other parts of the road network, such as intersections and motorway ramps. There exist great opportunities for adopting this proactive safety assessment approach to transform work zone safety for both roadworkers and motorists.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.