Preventing pollution attacks in multi-source network coding

Network coding is a method for achieving channel capacity in networks. The key idea is to allow network routers to linearly mix packets as they traverse the network so that recipients receive linear combinations of packets. Network coded systems are vulnerable to pollution attacks where a single malicious node floods the network with bad packets and prevents the receiver from decoding correctly. Cryptographic defenses to these problems are based on homomorphic signatures and MACs. These proposals, however, cannot handle mixing of packets from multiple sources, which is needed to achieve the full benefits of network coding. In this paper we address integrity of multi-source mixing. We propose a security model for this setting and provide a generic construction.

Lattice-based completely non-malleable public-key encryption in the standard model

An encryption scheme is non-malleable if giving an encryption of a message to an adversary does not increase its chances of producing an encryption of a related message (under a given public key). Fischlin introduced a stronger notion, known as complete non-malleability, which requires attackers to have negligible advantage, even if they are allowed to transform the public key under which the related message is encrypted. Ventre and Visconti later proposed a comparison-based definition of this security notion, which is more in line with the well-studied definitions proposed by Bellare et al. The authors also provide additional feasibility results by proposing two constructions of completely non-malleable schemes, one in the common reference string model using non-interactive zero-knowledge proofs, and another using interactive encryption schemes. Therefore, the only previously known completely non-malleable (and non-interactive) scheme in the standard model, is quite inefficient as it relies on generic NIZK approach. They left the existence of efficient schemes in the common reference string model as an open problem. Recently, two efficient public-key encryption schemes have been proposed by Libert and Yung, and Barbosa and Farshim, both of them are based on pairing identity-based encryption. At ACISP 2011, Sepahi et al. proposed a method to achieve completely non-malleable encryption in the public-key setting using lattices but there is no security proof for the proposed scheme. In this paper we review the mentioned scheme and provide its security proof in the standard model. Our study shows that Sepahi’s scheme will remain secure even for post-quantum world since there are currently no known quantum algorithms for solving lattice problems that perform significantly better than the best known classical (i.e., non-quantum) algorithms.

Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Novel sequential methods in dose-finding designs : extensions of the continual reassessment method

Dose-finding trials are a form of clinical data collection process in which the primary objective is to estimate an optimum dose of an investigational new drug when given to a patient. This thesis develops and explores three novel dose-finding design methodologies. All design methodologies presented in this thesis are pragmatic. They use statistical models, incorporate clinicians' prior knowledge efficiently, and prematurely stop a trial for safety or futility reasons. Designing actual dose-finding trials using these methodologies will minimize practical difficulties, improve efficiency of dose estimation, be flexible to stop early and reduce possible patient discomfort or harm.

Tethering for Selective Synthesis of 2,2′-Biphenols : the Acetal Method

2,2'-Biphenols are a large and diverse group of compounds with exceptional properties both as ligands and bioactive agents. Traditional methods for their synthesis by oxidative dimerisation are often problematic and lead to mixtures of ortho- and para-connected regioisomers. To compound these issues, an intermolecular dimerisation strategy is often inappropriate for the synthesis of heterodimers. The ‘acetal method’ provides a solution for these problems: stepwise tethering of two monomeric phenols enables heterodimer synthesis, enforces ortho regioselectivity and allows relatively facile and selective intramolecular reactions to take place. The resulting dibenzo[1,3]dioxepines have been analysed by quantum chemical calculations to obtain information about the activation barrier for ring flip between the enantiomers. Hydrolytic removal of the dioxepine acetal unit revealed the 2,2′-biphenol target.

Police officer in-vehicle discomfort : appointments carriage method and vehicle seat features

Musculoskeletal pain is commonly reported by police officers. A potential cause of officer discomfort is a mismatch between vehicle seats and the method used for carrying appointments. Twenty-five police officers rated their discomfort while seated in: (1) a standard police vehicle seat, and (2) a vehicle seat custom-designed for police use. Discomfort was recorded in both seats while wearing police appointments on: (1) a traditional appointments belt, and (2) a load-bearing vest / belt combination (LBV). Sitting in the standard vehicle seat and carrying appointments on a traditional appointments belt were both associated with significantly elevated discomfort. Four vehicle seat features were most implicated as contributing to discomfort: back rest bolster prominence; lumbar region support; seat cushion width; and seat cushion bolster depth. Authorising the carriage of appointments using a LBV is a lower cost solution with potential to reduce officer discomfort. Furthermore, the introduction of custom-designed vehicle seats should be considered.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

An efficient and effective localization method for networked disjoint top-view cameras

Disjoint top-view networked cameras are among the most commonly utilized networks in many applications. One of the open questions for these cameras' study is the computation of extrinsic parameters (positions and orientations), named extrinsic calibration or localization of cameras. Current approaches either rely on strict assumptions of the object motion for accurate results or fail to provide results of high accuracy without the requirement of the object motion. To address these shortcomings, we present a location-constrained maximum a posteriori (LMAP) approach by applying known locations in the surveillance area, some of which would be passed by the object opportunistically. The LMAP approach formulates the problem as a joint inference of the extrinsic parameters and object trajectory based on the cameras' observations and the known locations. In addition, a new task-oriented evaluation metric, named MABR (the Maximum value of All image points' Back-projected localization errors' L2 norms Relative to the area of field of view), is presented to assess the quality of the calibration results in an indoor object tracking context. Finally, results herein demonstrate the superior performance of the proposed method over the state-of-the-art algorithm based on the presented MABR and classical evaluation metric in simulations and real experiments.

A sub‒domain smoothed Galerkin method for solid mechanics problems

A sub‒domain smoothed Galerkin method is proposed to integrate the advantages of mesh‒free Galerkin method and FEM. Arbitrarily shaped sub‒domains are predefined in problems domain with mesh‒free nodes. In each sub‒domain, based on mesh‒free Galerkin weak formulation, the local discrete equation can be obtained by using the moving Kriging interpolation, which is similar to the discretization of the high‒order finite elements. Strain smoothing technique is subsequently applied to the nodal integration of sub‒domain by dividing the sub‒domain into several smoothing cells. Moreover, condensation of DOF can also be introduced into the local discrete equations to improve the computational efficiency. The global governing equations of present method are obtained on the basis of the scheme of FEM by assembling all local discrete equations of the sub‒domains. The mesh‒free properties of Galerkin method are retained in each sub‒domain. Several 2D elastic problems have been solved on the basis of this newly proposed method to validate its computational performance. These numerical examples proved that the newly proposed sub‒domain smoothed Galerkin method is a robust technique to solve solid mechanics problems based on its characteristics of high computational efficiency, good accuracy, and convergence.

Health monitoring of short-and medium-spen bridges using an improved modal strain energy method

Increasing the importance and use of infrastructures such as bridges, demands more effective structural health monitoring (SHM) systems. SHM has well addressed the damage detection issues through several methods such as modal strain energy (MSE). Many of the available MSE methods either have been validated for limited type of structures such as beams or their performance is not satisfactory. Therefore, it requires a further improvement and validation of them for different types of structures. In this study, an MSE method was mathematically improved to precisely quantify the structural damage at an early stage of formation. Initially, the MSE equation was accurately formulated considering the damaged stiffness and then it was used for derivation of a more accurate sensitivity matrix. Verification of the improved method was done through two plane structures: a steel truss bridge and a concrete frame bridge models that demonstrate the framework of a short- and medium-span of bridge samples. Two damage scenarios including single- and multiple-damage were considered to occur in each structure. Then, for each structure, both intact and damaged, modal analysis was performed using STRAND7. Effects of up to 5 per cent noise were also comprised. The simulated mode shapes and natural frequencies derived were then imported to a MATLAB code. The results indicate that the improved method converges fast and performs well in agreement with numerical assumptions with few computational cycles. In presence of some noise level, it performs quite well too. The findings of this study can be numerically extended to 2D infrastructures particularly short- and medium-span bridges to detect the damage and quantify it more accurately. The method is capable of providing a proper SHM that facilitates timely maintenance of bridges to minimise the possible loss of lives and properties.

A method for risk analysis across governance systems : a great barrier reef case study

Healthy governance systems are key to delivering sound environmental management outcomes from global to local scales. There are, however, surprisingly few risk assessment methods that can pinpoint those domains and sub-domains within governance systems that are most likely to influence good environmental outcomes at any particular scale, or those if absent or dysfunctional, most likely to prevent effective environmental management. This paper proposes a new risk assessment method for analysing governance systems. This method is then tested through its preliminary application to a significant real-world context: governance as it relates to the health of Australia's Great Barrier Reef (GBR). The GBR exists at a supra-regional scale along most of the north eastern coast of Australia. Brodie et al (2012 Mar. Pollut. Bull. 65 81-100) have recently reviewed the state and trend of the health of the GBR, finding that overall trends remain of significant concern. At the same time, official international concern over the governance of the reef has recently been signalled globally by the International Union for the Conservation of Nature (IUCN). These environmental and political contexts make the GBR an ideal candidate for use in testing and reviewing the application of improved tools for governance risk assessment. © 2013 IOP Publishing Ltd.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

A meshfree-based local Galerkin method with condensation of degree of freedom for elastic dynamic analysis

Condensation technique of degree of freedom is first proposed to improve the computational efficiency of meshfree method with Galerkin weak form for elastic dynamic analysis. In the present method, scattered nodes without connectivity are divided into several subsets by cells with arbitrary shape. Local discrete equation is established over each cell by using moving Kriging interpolation, in which the nodes that located in the cell are used for approximation. Then local discrete equations can be simplified by condensation of degree of freedom, which transfers equations of inner nodes to equations of boundary nodes based on cells. The global dynamic system equations are obtained by assembling all local discrete equations and are solved by using the standard implicit Newmark’s time integration scheme. In the scheme of present method, the calculation of each cell is carried out by meshfree method, and local search is implemented in interpolation. Numerical examples show that the present method has high computational efficiency and good accuracy in solving elastic dynamic problems.

CoRE : a context-aware relation extraction method for relation completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation, RC attempts at linking entity pairs between two entity lists under the relation. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Mini-scale method for nuclear run-on transcription assay in plants

We present a mini-scale method for nuclear run-on transcription assay. In our method, all the centrifuge steps can be carried out by using micro-tubes for short time (5 min each) throughout the process, including isolation of transcriptionally active nuclei and purification of labeled RNA after synthesis of RNA in isolated nuclei. The assay can be performed using a small amount of plant tissue, which enables analysis of developmental changes in transcriptional status of given genes in a single individual plant. Successful results were obtained using the tissues of flower and leaf of petunia and embryo of pea, suggesting that the method is potentially applicable to a variety of plant tissues.