Fast formulas for computing cryptographic pairings

The most powerful known primitive in public-key cryptography is undoubtedly elliptic curve pairings. Upon their introduction just over ten years ago the computation of pairings was far too slow for them to be considered a practical option. This resulted in a vast amount of research from many mathematicians and computer scientists around the globe aiming to improve this computation speed. From the use of modern results in algebraic and arithmetic geometry to the application of foundational number theory that dates back to the days of Gauss and Euler, cryptographic pairings have since experienced a great deal of improvement. As a result, what was an extremely expensive computation that took several minutes is now a high-speed operation that takes less than a millisecond. This thesis presents a range of optimisations to the state-of-the-art in cryptographic pairing computation. Both through extending prior techniques, and introducing several novel ideas of our own, our work has contributed to recordbreaking pairing implementations.

Automatic query expansion : a structural linguistic perspective

A user’s query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques ignore information about the dependencies that exist between words in natural language. However, more recent approaches have demonstrated that by explicitly modeling associations between terms significant improvements in retrieval effectiveness can be achieved over those that ignore these dependencies. State-of-the-art dependency-based approaches have been shown to primarily model syntagmatic associations. Syntagmatic associations infer a likelihood that two terms co-occur more often than by chance. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process will improve retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.

Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

Computer modeling of diffusion in biological tissues

Molecular-level computer simulations of restricted water diffusion can be used to develop models for relating diffusion tensor imaging measurements of anisotropic tissue to microstructural tissue characteristics. The diffusion tensors resulting from these simulations can then be analyzed in terms of their relationship to the structural anisotropy of the model used. As the translational motion of water molecules is essentially random, their dynamics can be effectively simulated using computers. In addition to modeling water dynamics and water-tissue interactions, the simulation software of the present study was developed to automatically generate collagen fiber networks from user-defined parameters. This flexibility provides the opportunity for further investigations of the relationship between the diffusion tensor of water and morphologically different models representing different anisotropic tissues.

Career-path analysis using optimal matching and self-organizing maps

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.

Impacts of plug-in electric vehicles on distribution networks

As a good solution to the shortage and environmental unfriendliness of fossil fuels, plug-in electric vehicles (PEVs) attract much interests of the public. To investigate the problems caused by the integration of numerous PEVs, a lot of research work has been done on the grid impacts of PEVs in aspects including thermal loading, voltage regulation, transformer loss of life, unbalance, losses, and harmonic distortion levels. This paper surveys the-state-of-the-art of the research in this area and outline three possible measures for a power grid company to make full use of PEVs.

Carbon dioxide reforming of methane to produce synthesis gas over metal-supported catalysts: State of the art

Carbon dioxide reforming of methane produces synthesis gas with a low hydrogen to carbon monoxide ratio, which is desirable for many industrial synthesis processes. This reaction also has very important environmental implications since both methane and carbon dioxide contribute to the greenhouse effect. Converting these gases into a valuable feedstock may significantly reduce the atmospheric emissions of CO2 and CH4. In this paper, we present a comprehensive review on the thermodynamics, catalyst selection and activity, reaction mechanism, and kinetics of this important reaction. Recently, research has centered on the development of catalysts and the feasible applications of this reaction in industry. Group VIII metals supported on oxides are found to be effective for this reason. However, carbon deposition causing catalyst deactivation is the major problem inhibiting the industrial application of the CO2/CH4 reaction. Ni-based catalysts impregnated on certain supports show carbon-free operation and thus attract much attention. To develop an effective catalyst for CO2 reforming of CH4 and accelerate the commercial application of the reaction, the following are identified to be the most important areas for future work: (1) selection of metal and support and studying the effect of their interaction on catalyst activity; (2) the effect of different promoter on catalyst activity; (3) the reaction mechanism and kinetics; and (4) pilot reactor performance and scale-up operation.

Building the computer mediated distance learning community: Issues and observations drawn from teaching and learning

This paper discusses computer mediated distance learning on a Master's level course in the UK and student perceptions of this as a quality learning environment.

The art of interpretation: Tracing logics of evaluation in Shaun Tan’s The Lost Thing (2000) and Andrew Ruhemann and Shaun Tan’s The Lost Thing (2010)

Celebration (and the celebritisation) of the Australian-ness of children’s authors who enjoy critical or commercial international success, and especially of those who win international prizes speaks to a desire to partake in both national and international cultural spheres. Prizing is often presumed to both guarantee and emerge from a creator's reputation at home and abroad. Australian artist and writer Shaun Tan has received a wide array of cultural and literary prizes, ranging from Australian book awards, to an Academy Award, to the Astrid Lindgren Memorial Prize. This paper considers logics of evaluation and interpretation as they can be traced in the intratextual, intertextual, and extratextual codes of Shaun Tan’s picture book, The Lost Thing (2000), the animated film adaptation of The Lost Thing (2010). It further considers the ways in which the desire for a global audience may necessitate an erasure of the national culture which is traded on in a global market.

A combined method for mitigating sparsity problem in tag recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

Visual front-end wars : Viola-Jones face detector vs Fourier Lucas-Kanade

The performance of visual speech recognition (VSR) systems are significantly influenced by the accuracy of the visual front-end. The current state-of-the-art VSR systems use off-the-shelf face detectors such as Viola- Jones (VJ) which has limited reliability for changes in illumination and head poses. For a VSR system to perform well under these conditions, an accurate visual front end is required. This is an important problem to be solved in many practical implementations of audio visual speech recognition systems, for example in automotive environments for an efficient human-vehicle computer interface. In this paper, we re-examine the current state-of-the-art VSR by comparing off-the-shelf face detectors with the recently developed Fourier Lucas-Kanade (FLK) image alignment technique. A variety of image alignment and visual speech recognition experiments are performed on a clean dataset as well as with a challenging automotive audio-visual speech dataset. Our results indicate that the FLK image alignment technique can significantly outperform off-the shelf face detectors, but requires frequent fine-tuning.

Detecting anomalous events at railway level crossings

Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.

Electrospraying of polymers with therapeutic molecules : state of the art

The encapsulation and release of bioactive molecules from polymeric vehicles represents the holy grail of drug and growth factor delivery therapies, whereby sustained and controlled release is crucial in eliciting a positive therapeutic effect. To this end, electrospraying is rapidly emerging as a popular technology for the production of polymeric particles containing bioactive molecules. Compared with traditional emulsion fabrication techniques, electrospraying has the potential to reduce denaturation of protein drugs and affords tighter regulation over particle size distribution and morphology. In this article, we review the importance of the electrospraying parameters that enable reproducible tailoring of the particles' physical and in vitro drug release characteristics, along with discussion of existing in vivo data. Controlled morphology and monodispersity of particles can be achieved with electrospraying, with high encapsulation efficiencies and without unfavorable denaturation of bioactive molecules throughout the process. Finally, the combination of electrospraying with electrospun scaffolds, with an emphasis on tissue regeneration is reviewed, depicting a technique in its relative infancy but holding great promise for the future of regenerative medicine.

Gestural, emergent and expressive : three research themes for haptic interaction

Drawing on three case studies of work in the fields of participatory design, interaction design and electronic arts, we reflect on the implications of these studies for haptic interface research. We propose three themes: gestural; emergent; and expressive; as signposts for a program of research into haptic interaction that could point the way towards novel approaches to haptic interaction and move us from optic to haptic ways of seeing.

The paradox of autistic art

Certain autistic children whose linguistic ability is virtually nonexistent can draw natural scenes from memory with astonishing accuracy. In particular their drawings display convincing perspective. In contrast, normal children of the same preschool age group and even untrained adults draw primitive schematics or symbols of objects which they can verbally identify. These are usually conceptual outlines devoid of detail. It is argued that the difference between autistic child artists and normal individuals is that autistic artists make no assumptions about what is to be seen in their environment. They have not formed mental representations of what is significant and consequently perceive all details as equally important. Equivalently, they do not impose visual or linguistic schema -- a process necessary for rapid conceptualisation in a dynamic existence, especially when the information presented to the eye is incomplete.