The delta-phase spectrum with application to voice activity detection and speaker recognition

For several reasons, the Fourier phase domain is less favored than the magnitude domain in signal processing and modeling of speech. To correctly analyze the phase, several factors must be considered and compensated, including the effect of the step size, windowing function and other processing parameters. Building on a review of these factors, this paper investigates a spectral representation based on the Instantaneous Frequency Deviation, but in which the step size between processing frames is used in calculating phase changes, rather than the traditional single sample interval. Reflecting these longer intervals, the term delta-phase spectrum is used to distinguish this from instantaneous derivatives. Experiments show that mel-frequency cepstral coefficients features derived from the delta-phase spectrum (termed Mel-Frequency delta-phase features) can produce broadly similar performance to equivalent magnitude domain features for both voice activity detection and speaker recognition tasks. Further, it is shown that the fusion of the magnitude and phase representations yields performance benefits over either in isolation.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Distributed object tracking with robot and disjoint camera networks

We describe a novel two stage approach to object localization and tracking using a network of wireless cameras and a mobile robot. In the first stage, a robot travels through the camera network while updating its position in a global coordinate frame which it broadcasts to the cameras. The cameras use this information, along with image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to track the objects. We present results with a nine node indoor camera network to demonstrate that this approach is feasible and offers acceptable level of accuracy in terms of object locations.

Access control : allocating resources to selfish agents

The ultimate goal of an authorisation system is to allocate each user the level of access they need to complete their job - no more and no less. This proves to be challenging in an organisational setting because on one hand employees need enough access to perform their tasks, while on the other hand more access will bring about an increasing risk of misuse - either intentionally, where an employee uses the access for personal benefit, or unintentionally through carelessness, losing the information or being socially engineered to give access to an adversary. With the goal of developing a more dynamic authorisation model, we have adopted a game theoretic framework to reason about the factors that may affect users’ likelihood to misuse a permission at the time of an access decision. Game theory provides a useful but previously ignored perspective in authorisation theory: the notion of the user as a self-interested player who selects among a range of possible actions depending on their pay-offs.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Privacy by information accountability for e-health systems

Privacy has become one of the main impediments for e-health in its advancement to providing better services to its consumers. Even though many security protocols are being developed to protect information from being compromised, privacy is still a major issue in healthcare where privacy protection is very important. When consumers are confident that their sensitive information is safe from being compromised, their trust in these services will be higher and would lead to better adoption of these systems. In this paper we propose a solution to the problem of patient privacy in e-health through an information accountability framework could enhance consumer trust in e-health services and would lead to the success of e-health services.

An approach to access control under uncertainty

In dynamic and uncertain environments such as healthcare, where the needs of security and information availability are difficult to balance, an access control approach based on a static policy will be suboptimal regardless of how comprehensive it is. The uncertainty stems from the unpredictability of users’ operational needs as well as their private incentives to misuse permissions. In Role Based Access Control (RBAC), a user’s legitimate access request may be denied because its need has not been anticipated by the security administrator. Alternatively, even when the policy is correctly specified an authorised user may accidentally or intentionally misuse the granted permission. This paper introduces a novel approach to access control under uncertainty and presents it in the context of RBAC. By taking insights from the field of economics, in particular the insurance literature, we propose a formal model where the value of resources are explicitly defined and an RBAC policy (entailing those predictable access needs) is only used as a reference point to determine the price each user has to pay for access, as opposed to representing hard and fast rules that are always rigidly applied.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Optimal budget allocation in budget-based access control

In dynamic and uncertain environments, where the needs of security and information availability are difficult to balance, an access control approach based on a static policy will be suboptimal regardless of how comprehensive it is. Risk-based approaches to access control attempt to address this problem by allocating a limited budget to users, through which they pay for the exceptions deemed necessary. So far the primary focus has been on how to incorporate the notion of budget into access control rather than what or if there is an optimal amount of budget to allocate to users. In this paper we discuss the problems that arise from a sub-optimal allocation of budget and introduce a generalised characterisation of an optimal budget allocation function that maximises organisations expected benefit in the presence of self-interested employees and costly audit.

Identity management : strengthening one-time password authentication through usability

Usability in HCI (Human-Computer Interaction) is normally understood as the simplicity and clarity with which the interaction with a computer program or a web site is designed. Identity management systems need to provide adequate usability and should have a simple and intuitive interface. The system should not only be designed to satisfy service provider requirements but it has to consider user requirements, otherwise it will lead to inconvenience and poor usability for users when managing their identities. With poor usability and a poor user interface with regard to security, it is highly likely that the system will have poor security. The rapid growth in the number of online services leads to an increasing number of different digital identities each user needs to manage. As a result, many people feel overloaded with credentials, which in turn negatively impacts their ability to manage them securely. Passwords are perhaps the most common type of credential used today. To avoid the tedious task of remembering difficult passwords, users often behave less securely by using low entropy and weak passwords. Weak passwords and bad password habits represent security threats to online services. Some solutions have been developed to eliminate the need for users to create and manage passwords. A typical solution is based on generating one-time passwords, i.e. passwords for single session or transaction usage. Unfortunately, most of these solutions do not satisfy scalability and/or usability requirements, or they are simply insecure. In this thesis, the security and usability aspects of contemporary methods for authentication based on one-time passwords (OTP) are examined and analyzed. In addition, more scalable solutions that provide a good user experience while at the same time preserving strong security are proposed.

3D ellipsoid fitting for multi-view gait recognition

Gait recognition approaches continue to struggle with challenges including view-invariance, low-resolution data, robustness to unconstrained environments, and fluctuating gait patterns due to subjects carrying goods or wearing different clothes. Although computationally expensive, model based techniques offer promise over appearance based techniques for these challenges as they gather gait features and interpret gait dynamics in skeleton form. In this paper, we propose a fast 3D ellipsoidal-based gait recognition algorithm using a 3D voxel model derived from multi-view silhouette images. This approach directly solves the limitations of view dependency and self-occlusion in existing ellipse fitting model-based approaches. Voxel models are segmented into four components (left and right legs, above and below the knee), and ellipsoids are fitted to each region using eigenvalue decomposition. Features derived from the ellipsoid parameters are modeled using a Fourier representation to retain the temporal dynamic pattern for classification. We demonstrate the proposed approach using the CMU MoBo database and show that an improvement of 15-20% can be achieved over a 2D ellipse fitting baseline.

Gait energy volumes and frontal gait recognition using depth images

Gait energy images (GEIs) and its variants form the basis of many recent appearance-based gait recognition systems. The GEI combines good recognition performance with a simple implementation, though it suffers problems inherent to appearance-based approaches, such as being highly view dependent. In this paper, we extend the concept of the GEI to 3D, to create what we call the gait energy volume, or GEV. A basic GEV implementation is tested on the CMU MoBo database, showing improvements over both the GEI baseline and a fused multi-view GEI approach. We also demonstrate the efficacy of this approach on partial volume reconstructions created from frontal depth images, which can be more practically acquired, for example, in biometric portals implemented with stereo cameras, or other depth acquisition systems. Experiments on frontal depth images are evaluated on an in-house developed database captured using the Microsoft Kinect, and demonstrate the validity of the proposed approach.

Tool-supported dataflow analysis of a security-critical embedded device

Defence organisations perform information security evaluations to confirm that electronic communications devices are safe to use in security-critical situations. Such evaluations include tracing all possible dataflow paths through the device, but this process is tedious and error-prone, so automated reachability analysis tools are needed to make security evaluations faster and more accurate. Previous research has produced a tool, SIFA, for dataflow analysis of basic digital circuitry, but it cannot analyse dataflow through microprocessors embedded within the circuit since this depends on the software they run. We have developed a static analysis tool that produces SIFA compatible dataflow graphs from embedded microcontroller programs written in C. In this paper we present a case study which shows how this new capability supports combined hardware and software dataflow analyses of a security critical communications device.

The conceptual and operational compatibility of data breach notification and information privacy laws

Mandatory data breach notification laws are a novel and potentially important legal instrument regarding organisational protection of personal information. These laws require organisations that have suffered a data breach involving personal information to notify those persons that may be affected, and potentially government authorities, about the breach. The Australian Law Reform Commission (ALRC) has proposed the creation of a mandatory data breach notification scheme, implemented via amendments to the Privacy Act 1988 (Cth). However, the conceptual differences between data breach notification law and information privacy law are such that it is questionable whether a data breach notification scheme can be solely implemented via an information privacy law. Accordingly, this thesis by publications investigated, through six journal articles, the extent to which data breach notification law was conceptually and operationally compatible with information privacy law. The assessment of compatibility began with the identification of key issues related to data breach notification law. The first article, Stakeholder Perspectives Regarding the Mandatory Notification of Australian Data Breaches started this stage of the research which concluded in the second article, The Mandatory Notification of Data Breaches: Issues Arising for Australian and EU Legal Developments (‘Mandatory Notification‘). A key issue that emerged was whether data breach notification was itself an information privacy issue. This notion guided the remaining research and focused attention towards the next stage of research, an examination of the conceptual and operational foundations of both laws. The second article, Mandatory Notification and the third article, Encryption Safe Harbours and Data Breach Notification Laws did so from the perspective of data breach notification law. The fourth article, The Conceptual Basis of Personal Information in Australian Privacy Law and the fifth article, Privacy Invasive Geo-Mashups: Privacy 2.0 and the Limits of First Generation Information Privacy Laws did so for information privacy law. The final article, Contextualizing the Tensions and Weaknesses of Information Privacy and Data Breach Notification Laws synthesised previous research findings within the framework of contextualisation, principally developed by Nissenbaum. The examination of conceptual and operational foundations revealed tensions between both laws and shared weaknesses within both laws. First, the distinction between sectoral and comprehensive information privacy legal regimes was important as it shaped the development of US data breach notification laws and their subsequent implementable scope in other jurisdictions. Second, the sectoral versus comprehensive distinction produced different emphases in relation to data breach notification thus leading to different forms of remedy. The prime example is the distinction between market-based initiatives found in US data breach notification laws compared to rights-based protections found in the EU and Australia. Third, both laws are predicated on the regulation of personal information exchange processes even though both laws regulate this process from different perspectives, namely, a context independent or context dependent approach. Fourth, both laws have limited notions of harm that is further constrained by restrictive accountability frameworks. The findings of the research suggest that data breach notification is more compatible with information privacy law in some respects than others. Apparent compatibilities clearly exist as both laws have an interest in the protection of personal information. However, this thesis revealed that ostensible similarities are founded on some significant differences. Data breach notification law is either a comprehensive facet to a sectoral approach or a sectoral adjunct to a comprehensive regime. However, whilst there are fundamental differences between both laws they are not so great to make them incompatible with each other. The similarities between both laws are sufficient to forge compatibilities but it is likely that the distinctions between them will produce anomalies particularly if both laws are applied from a perspective that negates contextualisation.

The Measurement of Information System Use: Preliminary Considerations

DeLone and McLean (1992, p. 16) argue that the concept of “system use” has suffered from a “too simplistic definition.” Despite decades of substantial research on system use, the concept is yet to receive strong theoretical scrutiny. Many measures of system use and the development of measures have been often idiosyncratic and lack credibility or comparability. This paper reviews various attempts at conceptualization and measurement of system use and then proposes a re-conceptualization of it as “the level of incorporation of an information system within a user’s processes.” The definition is supported with the theory of work systems, system, and Key-User-Group considerations. We then go on to develop the concept of a Functional- Interface-Point (FIP) and four dimensions of system usage: extent, the proportion of the FIPs used by the business process; frequency, the rate at which FIPs are used by the participants in the process; thoroughness, the level of use of information/functionality provided by the system at an FIP; and attitude towards use, a set of measures that assess the level of comfort, degree of respect and the challenges set forth by the system. The paper argues that the automation level, the proportion of the business process encoded by the information system has a mediating impact on system use. The article concludes with a discussion of some implications of this re-conceptualization and areas for follow on research.