User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor-intensive, time consuming and expensive. Remote sensing technologies represent an attractive and cost-effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection, and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments, we call this approach PTAGS. Improved object recognition is achieved by fusing information from multi-sensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches.

Turbulent measurements in a small subtropical estuary with semidiurnal tides

Since predictions of scalar dispersion in small estuaries can rarely be predicted accurately, new field measurements were conducted continuously at relatively high frequency for up to 50 h (per investigation) in a small subtropical estuary with semidiurnal tides. The bulk flow parameters varied in time with periods comparable to tidal cycles and other large-scale processes. The turbulence properties depended upon the instantaneous local flow properties. They were little affected by the flow history, but their structure and temporal variability were influenced by a variety of parameters including the tidal conditions and bathymetry. A striking feature of the data sets was the large fluctuations in all turbulence characteristics during the tidal cycle, and basic differences between neap and spring tide turbulence.

Improving self organizing capabilities of local communities for sustainable development in decentralized Indonesia : the roles of procedural justice and social learning

The development planning process introduced under Law No. 25/2004 is said to be a better approach to increase public participation in decentralised Indonesia. This Law has introduced planning mechanisms, called Musyawarah Perencanaan Pembangunan (musrenbang), to provide a forum for development planning. In spite of the expressed intention of these mechanisms to improve public participation, some empirical observations have cast doubt on the outcomes. As a result, some local governments have tried to provide alternative mechanisms for participatory local development planning processes. Since planning constitutes one of the most effective ways to improve community empowerment, this paper aims to examine the extent to which the alternative local development planning process in Indonesia provides sufficient opportunities to improve the self organising capabilities of communities to sustain development programs to meet local needs. In so doing, this paper explores the key elements and approaches of the concept of community empowerment and shows how they can be incorporated within planning processes. Based on this, it then examines the problems encountered by musrenbang in increasing community empowerment. Having done this, it is argued that to change current unfavourable outcomes, procedural justice and social learning approaches need to be incorporated as pathways to community empowerment. Lastly the capacity of an alternative local planning process, called Sistem Dukungan (SISDUK), introduced in South Sulawesi, offering scope to incorporate procedural justice and social learning is explored as a means to improve the self organizing capabilities of local communities.

Towards condition-invariant sequence-based route recognition

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountainbiking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected.

Crime: local and global

Crime: Local and Global covers the way local events (such as prostitution) have wider aspects than previously thought. Links with people traffickers, international organised crime and violence cannot be ignored any longer. Each crime or area of activity selected within this text has a global reach, and is made ever more possible due to the way globalisation has opened up markets, both legitimate and illegitimate. The book's approach and scope emphasises that we can no longer view 'crime' as something which occurs within certain jurisdictions, at certain times and in particular places. For example, the chapter on cybercrime highlights the 'illegal' acts that can be perpetrated by second lifers, anywhere in the world, but are they a crime?

Growing local participation through long term design

Our research explores the design of networked technologies to facilitate local suburban communications and to encourage people to engage with their local community. While there are many investigations of interaction designs for networked technologies, most research utilises small exercises, workshops or other short-term studies to investigate interaction designs. However, we have found these short-term methods to be ineffective in the context of understanding local community interaction. Moreover we find that people are resistant to putting their time into workshops and exercises, understandably so because these are academic practices, not local community practices. Our contribution is to detail a long term embedded design approach in which we interact with the community over the long term in the course of normal community goings-on with an evolving exploratory prototype. This paper discusses the embedded approach to working in the wild for extended field research.

An exploration of feature detector performance in the thermal-infrared modality

Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of feature detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on feature detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of feature counts was found to improve the repeatability performance of several detectors. Most other image transformations had predictable effects on feature stability. The best-performing detector varied considerably depending on the nature of the scene and the test.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

A semi-local method for iterative depth-map refinement

We present an iterative hierarchical algorithm for multi-view stereo. The algorithm attempts to utilise as much contextual information as is available to compute highly accurate and robust depth maps. There are three novel aspects to the approach: 1) firstly we incrementally improve the depth fidelity as the algorithm progresses through the image pyramid; 2) secondly we show how to incorporate visual hull information (when available) to constrain depth searches; and 3) we show how to simultaneously enforce the consistency of the depth-map by continual comparison with neighbouring depth-maps. We show that this approach produces highly accurate depth-maps and, since it is essentially a local method, is both extremely fast and simple to implement.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

How does the size and shape of local populations in China compare to general anthropometric surveys currently used for product design?

Anthropometry has long been used for a range of ergonomic applications & product design. Although products are often designed for specific cohorts, anthropometric data are typically sourced from large scale surveys representative of the general population. Additionally, few data are available for emerging markets like China and India. This study measured 80 Chinese males that were representative of a specific cohort targeted for the design of a new product. Thirteen anthropometric measurements were recorded and compared to two large databases that represented a general population, a Chinese database and a Western database. Substantial differences were identified between the Chinese males measured in this study and both databases. The subjects were substantially taller, heavier and broader than subjects in the older Chinese database. However, they were still substantially smaller, lighter and thinner than Western males. Data from current Western anthropometric surveys are unlikely to accurately represent the target population for product designers and manufacturers in emerging markets like China.

Feature-domain super-resolution framework for gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.

Crime : Local and Global

Crime: Local and Global covers the way local events (such as prostitution) have wider aspects than previously thought. Links with people traffickers, international organised crime and violence cannot be ignored any longer. Each crime or area of activity selected within this text has a global reach, and is made ever more possible due to the way globalisation has opened up markets, both legitimate and illegitimate. The book's approach and scope emphasises that we can no longer view 'crime' as something which occurs within certain jurisdictions, at certain times and in particular places. For example, the chapter on cybercrime highlights the 'illegal' acts that can be perpetrated by second lifers, anywhere in the world, but are they a crime?

CAT-SLAM : probabilistic localisation and mapping using a continuous appearance-based trajectory

This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao–Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.