Fast content-based file type identification

Digital forensic examiners often need to identify the type of a file or file fragment based only on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds classification by sampling several blocks from the file. Experimental results demonstrate that up to a fifteen-fold reduction in file size analysis time can be achieved with limited impact on accuracy.

An approach to statistical lip modelling for speaker identification via chromatic feature extraction

This paper presents a novel technique for the tracking of moving lips for the purpose of speaker identification. In our system, a model of the lip contour is formed directly from chromatic information in the lip region. Iterative refinement of contour point estimates is not required. Colour features are extracted from the lips via concatenated profiles taken around the lip contour. Reduction of order in lip features is obtained via principal component analysis (PCA) followed by linear discriminant analysis (LDA). Statistical speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments performed on the M2VTS¹ database, show encouraging results

The use of temporal speech and lip information for multi-modal speaker identification via multi-stream HMMs

Investigates the use of temporal lip information, in conjunction with speech information, for robust, text-dependent speaker identification. We propose that significant speaker-dependent information can be obtained from moving lips, enabling speaker recognition systems to be highly robust in the presence of noise. The fusion structure for the audio and visual information is based around the use of multi-stream hidden Markov models (MSHMM), with audio and visual features forming two independent data streams. Recent work with multi-modal MSHMMs has been performed successfully for the task of speech recognition. The use of temporal lip information for speaker identification has been performed previously (T.J. Wark et al., 1998), however this has been restricted to output fusion via single-stream HMMs. We present an extension to this previous work, and show that a MSHMM is a valid structure for multi-modal speaker identification

Monitoring identity of past, current and future : a performance management system perspective

This paper seeks to explore how organisations can effectively use performance management systems (PMS) to monitor collective identities. The monitoring of relationships between identity and an influential PMS—the balanced scorecard (BSC)—are explored. Drawing from identity and management accounting literature, this paper argues that identity products, patternings and processes are commonly positioned, monitored and interpreted through the multiple perspectives and levels of the BSC. Specifically, human, technical and organisational capital under the Learning and Growth perspective of the BSC can incorporate various identity measures that sustain the relative, distinctive and fluid nature of identities. The value of this research is to strengthen the theoretical grounds which position identity as an important dimension of organisational capital in PMS.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

A descriptive approach for power system stability and security assessment

Power system dynamic analysis and security assessment are becoming more significant today due to increases in size and complexity from restructuring, emerging new uncertainties, integration of renewable energy sources, distributed generation, and micro grids. Precise modelling of all contributed elements/devices, understanding interactions in detail, and observing hidden dynamics using existing analysis tools/theorems are difficult, and even impossible. In this chapter, the power system is considered as a continuum and the propagated electomechanical waves initiated by faults and other random events are studied to provide a new scheme for stability investigation of a large dimensional system. For this purpose, the measured electrical indices (such as rotor angle and bus voltage) following a fault in different points among the network are used, and the behaviour of the propagated waves through the lines, nodes, and buses is analyzed. The impact of weak transmission links on a progressive electromechanical wave using energy function concept is addressed. It is also emphasized that determining severity of a disturbance/contingency accurately, without considering the related electromechanical waves, hidden dynamics, and their properties is not secure enough. Considering these phenomena takes heavy and time consuming calculation, which is not suitable for online stability assessment problems. However, using a continuum model for a power system reduces the burden of complex calculations

Identification of key competences of DB clients in the construction market of China

The design-build system has been demonstrated as an effective delivery method and gained popularity worldwide. Although there are an increasing number of clients adopting DB method in China, most of them remain inexperienced with method. The objective of this study is therefore to identify the key competences that a client or its consultant should possess to ensure the success of DB projects. Face-to-face interviews and a two-round Delphi questionnaire survey were conducted to find the following six key competences of clients, which include the (1) ability to clearly articulate project scope and objectives; (2) financial capacity for DB projects; (3) capability in contract management; (4) adequate staff or consulting team; (5) effective coordination with contractors and (6) experience with similar DB projects. This study will hopefully provide clients with measures to evaluate their DB competence and further promote their understanding of DB system in the PRC.

Exploring the contested role of mandatory reporting laws in the identification of severe child abuse and neglect

Significant numbers of children are severely abused and neglected by parents and caregivers. Infants and very young children are the most vulnerable and are unable to seek help. To identify these situations and enable child protection and the provision of appropriate assistance, many jurisdictions have enacted ‘mandatory reporting laws’ requiring designated professionals such as doctors, nurses, police and teachers to report suspected cases of severe child abuse and neglect. Other jurisdictions have not adopted this legislative approach, at least partly motivated by a concern that the laws produce dramatic increases in unwarranted reports, which, it is argued, lead to investigations which infringe on people’s privacy, cause trauma to innocent parents and families, and divert scarce government resources from deserving cases. The primary purpose of this paper is to explore the extent to which opposition to mandatory reporting laws is valid based on the claim that the laws produce ‘overreporting’. The first part of this paper revisits the original mandatory reporting laws, discusses their development into various current forms, explains their relationship with policy and common law reporting obligations, and situates them in the context of their place in modern child protection systems. This part of the paper shows that in general, contemporary reporting laws have expanded far beyond their original conceptualisation, but that there is also now a deeper understanding of the nature, incidence, timing and effects of different types of severe maltreatment, an awareness that the real incidence of maltreatment is far higher than that officially recorded, and that there is strong evidence showing the majority of identified cases of severe maltreatment are the result of reports by mandated reporters. The second part of this paper discusses the apparent effect of mandatory reporting laws on ‘overreporting’ by referring to Australian government data about reporting patterns and outcomes, with a particular focus on New South Wales. It will be seen that raw descriptive data about report numbers and outcomes appear to show that reporting laws produce both desirable consequences (identification of severe cases) and problematic consequences (increased numbers of unsubstantiated reports). Yet, to explore the extent to which the data supports the overreporting claim, and because numbers of unsubstantiated reports alone cannot demonstrate overreporting, this part of the paper asks further questions of the data. Who makes reports, about which maltreatment types, and what are the outcomes of those reports? What is the nature of these reports; for example, to what extent are multiple numbers of reports made about the same child? What meaning can be attached to an ‘unsubstantiated’ report, and can such reports be used to show flaws in reporting effectiveness and problems in reporting laws? It will be suggested that available evidence from Australia is not sufficiently detailed or strong to demonstrate the overreporting claim. However, it is also apparent that, whether adopting an approach based on public health and or other principles, much better evidence about reporting needs to be collected and analyzed. As well, more nuanced research needs to be conducted to identify what can reasonably be said to constitute ‘overreports’, and efforts must be made to minimize unsatisfactory reporting practice, informed by the relevant jurisdiction’s context and aims. It is also concluded that, depending on the jurisdiction, the available data may provide useful indicators of positive, negative and unanticipated effects of specific components of the laws, and of the strengths, weaknesses and needs of the child protection system.

The use of virtual prototyping for hazard identification in the early design stage

Purpose: The construction industry is well known for its high accident rate and many practitioners consider a preventative approach to be the most important means of bringing about improvements. This paper addresses previous research and the weaknesses of existing preventative approaches and a new application is described and illustrated involving the use of a multi-dimensional simulation tool - Construction Virtual Prototyping (CVP). Methodology: A literature review was conducted to investigate previous studies of hazard identification and safety management and to develop the new approach. Due to weaknesses in current practice, the research study explored the use of computer simulation techniques to create virtual environments where users can explore and identify construction hazards. Specifically, virtual prototyping technology was deployed to develop typical construction scenarios in which unsafe or hazardous incidents occur. In a case study, the users’ performance was evaluated their responses to incidents within the virtual environment and the effectiveness of the computer simulation system established though interviews with the safety project management team. Findings: The opinions and suggestions provided by the interviewees led to the initial conclusion that the simulation tool was useful in assisting the safety management team’s hazard identification process during the early design stage. Originality: The research introduces an innovative method to support the management teams’ reviews of construction site safety. The system utilises three-dimensional modelling and four-dimensional simulation of worker behaviour, a configuration that has previously not been employed in construction simulations. An illustration of the method’s use is also provided, together with a consideration of its strengths and weaknesses.

Concealed substance identification using a defocused inverse spatially offset Raman spectrometer

We report an inverse Spatially Offset Raman Spectrometer capable of non-invasively identifying packaged substances from a distance. Usual inverse SORS spectrometer has a non-contact distance that is equivalent to the focal distance of the collection system. In this work we demonstrate the defocused geometry with a modified data analysis method capable of making inverse SORS measurements from a distance greater than the focal distance of the collection lenses. With the defocused geometry we were able to detect acetaminophen, concealed inside a 2 mm thick plastic bottle, at a non-contact distance of 30 cm.

An ingenious decision support system for remote health monitoring of pregnant women

Maternal deaths have been a critical issue for women living in rural and remote areas. The need to travel long distances, the shortage of primary care providers such as physicians, specialists and nurses, and the closing of small hospitals have been problems identified in many rural areas. Some research work has been undertaken and a few techniques have been developed to remotely measure the physiological condition of pregnant women through sophisticated ultrasound equipment. There are numerous ways to reduce maternal deaths, and an important step is to select the right approaches to achieving this reduction. One such approach is the provision of decision support systems in rural and remote areas. Decision support systems (DSSs) have already shown a great potential in many health fields. This thesis proposes an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s á and Classification Tree were incorporated in the iDSS. The decision support system was developed with significant variables such as: Place of residence, Seeing the same doctor, Education, Tetanus injection, Baby weight, Previous baby born, Place of birth, Assisted delivery, Pregnancy parity, Doctor visits and Occupation. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcomes of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women. On conditional system was sent and validated by the gynaecologist. Another outcome of ingenious decision support system was to provide better pregnancy health awareness and reduce long distance travel, especially for women in rural areas. The proposed system has qualities such as usefulness, accuracy and accessibility.

A database for person re-identification in multi-camera surveillance networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.

An expressed sequence tag (EST) library for Drosophila serrata, a model system for sexual selection and climatic adaptation studies

The native Australian fly Drosophila serrata belongs to the highly speciose montium subgroup of the melanogaster species group. It has recently emerged as an excellent model system with which to address a number of important questions, including the evolution of traits under sexual selection and traits involved in climatic adaptation along latitudinal gradients. Understanding the molecular genetic basis of such traits has been limited by a lack of genomic resources for this species. Here, we present the first expressed sequence tag (EST) collection for D. serrata that will enable the identification of genes underlying sexually-selected phenotypes and physiological responses to environmental change and may help resolve controversial phylogenetic relationships within the montium subgroup.

Identification of GABA receptors in chick cornea

Purpose: The cornea has an important role in vision, is highly innervated and many neurotransmitter receptors are present, e.g., muscarine, melatonin, and dopamine receptors. γ-aminobutyric acid (GABA) is the most important inhibitory neurotransmitter in the retina and central nervous system, but it is unknown whether GABA receptors are present in cornea. The aim of this study was to determine if GABA receptors are located in chick cornea. Methods: Corneal tissues were collected from 25, 12-day-old chicks. Real time PCR, western blot, and immunohistochemistry were used to determine whether alpha1 GABAA, GABAB, and rho1 GABAC receptors were expressed and located in chick cornea. Results: Corneal tissue was positive for alpha1 GABAA and rho1 GABAC receptor mRNA (PCR) and protein (western blot) expression but was negative for GABAB receptor mRNA and protein. Alpha1 GABAA and rho1 GABAC receptor protein labeling was observed in the corneal epithelium using immunohistochemistry. Conclusions: These investigations clearly show that chick cornea possesses alpha1 GABAA, and rho1 GABAC receptors, but not GABAB receptors. The purpose of the alpha1 GABAA and rho1 GABAC receptors in cornea is a fascinating unexplored question.

An ingenuous Decision Support System (iDSS) approach for remote area pregnant women

This paper addresses development of an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s α and Classification Tree were incorporated in the iDSS. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcome of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women.