Dessy : desktop search and synchronization

Current smartphones have a storage capacity of several gigabytes. More and more information is stored on mobile devices. To meet the challenge of information organization, we turn to desktop search. Users often possess multiple devices, and synchronize (subsets of) information between them. This makes file synchronization more important. This thesis presents Dessy, a desktop search and synchronization framework for mobile devices. Dessy uses desktop search techniques, such as indexing, query and index term stemming, and search relevance ranking. Dessy finds files by their content, metadata, and context information. For example, PDF files may be found by their author, subject, title, or text. EXIF data of JPEG files may be used in finding them. User–defined tags can be added to files to organize and retrieve them later. Retrieved files are ranked according to their relevance to the search query. The Dessy prototype uses the BM25 ranking function, used widely in information retrieval. Dessy provides an interface for locating files for both users and applications. Dessy is closely integrated with the Syxaw file synchronizer, which provides efficient file and metadata synchronization, optimizing network usage. Dessy supports synchronization of search results, individual files, and directory trees. It allows finding and synchronizing files that reside on remote computers, or the Internet. Dessy is designed to solve the problem of efficient mobile desktop search and synchronization, also supporting remote and Internet search. Remote searches may be carried out offline using a downloaded index, or while connected to the remote machine on a weak network. To secure user data, transmissions between the Dessy client and server are encrypted using symmetric encryption. Symmetric encryption keys are exchanged with RSA key exchange. Dessy emphasizes extensibility. Also the cryptography can be extended. Users may tag their files with context tags and control custom file metadata. Adding new indexed file types, metadata fields, ranking methods, and index types is easy. Finding files is done with virtual directories, which are views into the user’s files, browseable by regular file managers. On mobile devices, the Dessy GUI provides easy access to the search and synchronization system. This thesis includes results of Dessy synchronization and search experiments, including power usage measurements. Finally, Dessy has been designed with mobility and device constraints in mind. It requires only MIDP 2.0 Mobile Java with FileConnection support, and Java 1.5 on desktop machines.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Kertooko luonnon järjestys suunnittelusta? : Michael Behe älykkään suunnittelun ajatuksen puolustajana

This study presents a systematical analysis of biochemist Michael Behe's thinking. Behe is a prominent defender of the Intelligent Design Movement which has gaines influence particularly in the United States, but also in elsewhere. At the core of his thinking is the idea of intelligent design, according to which the order of the cosmos and of living things is the handiwork of a non-human intelligence. This "design argument" had previously been popular in the tradition of natural theology. Behe attempts to base his argument on the findings of 20th century biology, however. It has been revealed by biochemistry that cells, formerly thought to be simple, in fact contain complex structures, for instance the bacterial flagellum, which are reminiscent of the machines built by humans. According to Behe these can be believably explained only by referring to intelligent design, not by invoking darwinian natural laws. My analysis aims to understand Behe's thought on intelligent design, to bring forward its connections to intellectual history and worldviews, and to study whether Behe has formulated his argument so as to avoid common criticisms directed against design arguments. I use a large amount literature and refer to diverse writers participating in the intelligent design debate. The results of the analysis are as follows. Behe manages to avoid a large amount of classical criticisms against the design argument, and new criticisms have to be developed to meet his argument. Secondly, positions on intelligent design appear to be linked to larger philosophical and religious worldviews.vaan myös maailmankuvat ja uskonnolliset näkemykset.

Moiré Displacement Transducer for N/C Systems

Noncontact method of sensing accurately the magnitude and direction of displacements is essential in systems such as the numerically controlled machines. A displacement transducer, using Moiré transmission gratings is described. The notable feature of this instrument is that it requires only gratings of small lengths, even for measurement of large displacements.

Formal network behaviour analysis using model checking

In this research we modelled computer network devices to ensure their communication behaviours meet various network standards. By modelling devices as finite-state machines and examining their properties in a range of configurations, we discovered a flaw in a common network protocol and produced a technique to improve organisations' network security against data theft.

Vibrations of Multifiber Composite Shells-Some Numerical Results

A study of vibrations of multifiber composite shells is presented. Special attention is paid to the effect of composition of different fibers on the frequency spectrum of a freely vibrating cylindrical shell. The numerical results indicate clustering of frequency spectrum of a freely vibrating cylindrical composite shell as compared with the isotropic shell, and the spectrum varies considerably with the composition of the constituent materials.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

A strategy for scheduling tightly coupled parallel applications on clusters

Although various strategies have been developed for scheduling parallel applications with independent tasks, very little work exists for scheduling tightly coupled parallel applications on cluster environments. In this paper, we compare four different strategies based on performance models of tightly coupled parallel applications for scheduling the applications on clusters. In addition to algorithms based on existing popular optimization techniques, we also propose a new algorithm called Box Elimination that searches the space of performance model parameters to determine the best schedule of machines. By means of real and simulation experiments, we evaluated the algorithms on single cluster and multi-cluster setups. We show that our Box Elimination algorithm generates up to 80% more efficient schedule than other algorithms. We also show that the execution times of the schedules produced by our algorithm are more robust against the performance modeling errors.

Wear Next: An Exploration into the Future of Wearable Technology

Technology is increasingly infiltrating all aspects of our lives and the rapid uptake of devices that live near, on or in our bodies are facilitating radical new ways of working, relating and socialising. This distribution of technology into the very fabric of our everyday life creates new possibilities, but also raises questions regarding our future relationship with data and the quantified self. By embedding technology into the fabric of our clothes and accessories, it becomes ‘wearable’. Such ‘wearables’ enable the acquisition of and the connection to vast amounts of data about people and environments in order to provide life-augmenting levels of interactivity. Wearable sensors for example, offer the potential for significant benefits in the future management of our wellbeing. Fitness trackers such as ‘Fitbit’ and ‘Garmen’ provide wearers with the ability to monitor their personal fitness indicators while other wearables provide healthcare professionals with information that improves diagnosis. While the rapid uptake of wearables may offer unique and innovative opportunities, there are also concerns surrounding the high levels of data sharing that come as a consequence of these technologies. As more ‘smart’ devices connect to the Internet, and as technology becomes increasingly available (e.g. via Wi-Fi, Bluetooth), more products, artefacts and things are becoming interconnected. This digital connection of devices is called The ‘Internet of Things’ (IoT). IoT is spreading rapidly, with many traditionally non-online devices becoming increasingly connected; products such as mobile phones, fridges, pedometers, coffee machines, video cameras, cars and clothing. The IoT is growing at a rapid rate with estimates indicating that by 2020 there will be over 25 billion connected things globally. As the number of devices connected to the Internet increases, so too does the amount of data collected and type of information that is stored and potentially shared. The ability to collect massive amounts of data - known as ‘big data’ - can be used to better understand and predict behaviours across all areas of research from societal and economic to environmental and biological. With this kind of information at our disposal, we have a more powerful lens with which to perceive the world, and the resulting insights can be used to design more appropriate products, services and systems. It can however, also be used as a method of surveillance, suppression and coercion by governments or large organisations. This is becoming particularly apparent in advertising that targets audiences based on the individual preferences revealed by the data collected from social media and online devices such as GPS systems or pedometers. This type of technology also provides fertile ground for public debates around future fashion, identity and broader social issues such as culture, politics and the environment. The potential implications of these type of technological interactions via wearables, through and with the IoT, have never been more real or more accessible. But, as highlighted, this interconnectedness also brings with it complex technical, ethical and moral challenges. Data security and the protection of privacy and personal information will become ever more present in current and future ethical and moral debates of the 21st century. This type of technology is also a stepping-stone to a future that includes implantable technology, biotechnologies, interspecies communication and augmented humans (cyborgs). Technologies that live symbiotically and perpetually in our bodies, the built environment and the natural environment are no longer the stuff of science fiction; it is in fact a reality. So, where next?... The works exhibited in Wear Next_ provide a snapshot into the broad spectrum of wearables in design and in development internationally. This exhibition has been curated to serve as a platform for enhanced broader debate around future technology, our mediated future-selves and the evolution of human interactions. As you explore the exhibition, may we ask that you pause and think to yourself, what might we... Wear Next_? WEARNEXT ONLINE LISTINGS AND MEDIA COVERAGE: http://indulgemagazine.net/wear-next/ http://www.weekendnotes.com/wear-next-exhibition-gallery-artisan/ http://concreteplayground.com/brisbane/event/wear-next_/ http://www.nationalcraftinitiative.com.au/news_and_events/event/48/wear-next http://bneart.com/whats-on/wear-next_/ http://creativelysould.tumblr.com/post/124899079611/creative-weekend-art-edition http://www.abc.net.au/radionational/programs/breakfast/smartly-dressed-the-future-of-wearable-technology/6744374 http://couriermail.newspaperdirect.com/epaper/viewer.aspx RADIO COVERAGE http://www.abc.net.au/radionational/programs/breakfast/wear-next-exhibition-whats-next-for-wearable-technology/6745986 TELEVISION COVERAGE http://www.abc.net.au/radionational/programs/breakfast/wear-next-exhibition-whats-next-for-wearable-technology/6745986 https://au.news.yahoo.com/video/watch/29439742/how-you-could-soon-be-wearing-smart-clothes/#page1

Analysis of DNA sequence transformations on grids

Study of the evolution of species or organisms is essential for various biological applications. Evolution is typically studied at the molecular level by analyzing the mutations of DNA sequences of organisms. Techniques have been developed for building phylogenetic or evolutionary trees for a set of sequences. Though phylogenetic trees capture the overall evolutionary relationships among the sequences, they do not reveal fine-level details of the evolution. In this work, we attempt to resolve various fine-level sequence transformation details associated with a phylogenetic tree using cellular automata. In particular, our work tries to determine the cellular automata rules for neighbor-dependent mutations of segments of DNA sequences. We also determine the number of time steps needed for evolution of a progeny from an ancestor and the unknown segments of the intermediate sequences in the phylogenetic tree. Due to the existence of vast number of cellular automata rules, we have developed a grid system that performs parallel guided explorations of the rules on grid resources. We demonstrate our techniques by conducting experiments on a grid comprising machines in three countries and obtaining potentially useful statistics regarding evolutions in three HIV sequences. In particular, our work is able to verify the phenomenon of neighbor-dependent mutations and find that certain combinations of neighbor-dependent mutations, defined by a cellular automata rule, occur with greater than 90% probability. We also find the average number of time steps for mutations for some branches of phylogenetic tree over a large number of possible transformations with standard deviations less than 2.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Ultrastructure of Biofilms Formed by Bacteria from Industrial Processes

Microorganisms exist predominantly as sessile multispecies communities in natural habitats. Most bacterial species can form these matrix-enclosed microbial communities called biofilms. Biofilms occur in a wide range of environments, on every surface with sufficient moisture and nutrients, also on surfaces in industrial settings and engineered water systems. This unwanted biofilm formation on equipment surfaces is called biofouling. Biofouling can significantly decrease equipment performance and lifetime and cause contamination and impaired quality of the industrial product. In this thesis we studied bacterial adherence to abiotic surfaces by using coupons of stainless steel coated or not coated with fluoropolymer or diamond like carbon (DLC). As model organisms we used bacterial isolates from paper machines (Meiothermus silvanus, Pseudoxanthomonas taiwanensis and Deinococcus geothermalis) and also well characterised species isolated from medical implants (Staphylococcus epidermidis). We found that coating of steel surface with these materials reduced its tendency towards biofouling: Fluoropolymer and DLC coatings repelled all four biofilm formers on steel. We found great differences between bacterial species in their preference of surfaces to adhere as well as their ultrastructural details, like number and thickness of adhesion organelles they expressed. These details responded differently towards the different surfaces they adhered to. We further found that biofilms of D. geothermalis formed on titanium dioxide coated coupons of glass, steel and titanium, were effectively removed by photocatalytic action in response to irradiation at 360 nm. However, on non-coated glass or steel surfaces irradiation had no detectable effect on the amount of bacterial biomass. We showed that the adhesion organelles of bacteria on illuminated TiO2 coated coupons were complety destroyed whereas on non-coated coupons they looked intact when observed by microscope. Stainless steel is the most widely used material for industrial process equipments and surfaces. The results in this thesis showed that stainless steel is prone to biofouling by phylogenetically distant bacterial species and that coating of the steel may offer a tool for reduced biofouling of industrial equipment. Photocatalysis, on the other hand, is a potential technique for biofilm removal from surfaces in locations where high level of hygiene is required. Our study of natural biofilms on barley kernel surfaces showed that also there the microbes possessed adhesion organelles visible with electronmicroscope both before and after steeping. The microbial community of dry barley kernels turned into a dense biofilm covered with slimy extracellular polymeric substance (EPS) in the kernels after steeping in water. Steeping is the first step in malting. We also presented evidence showing that certain strains of Lactobacillus plantarum and Wickerhamomyces anomalus, when used as starter cultures in the steeping water, could enter the barley kernel and colonise the tissues of the barley kernel. By use of a starter culture it was possible to reduce the extensive production of EPS, which resulted in a faster filtration of the mash.

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.