Staged homicides: An examination of common features of faked burglaries, suicides, accidents and car accidents

A staged crime scene involves deliberate alteration of evidence by the offender to simulate events that did not occur for the purpose of misleading authorities (Geberth, 2006; Turvey, 2000). This study examined 115 staged homicides from the USA to determine common elements; victim and perpetrator characteristics; and specific features of different types of staged scenes. General characteristics include: multiple victims and offenders; a previous relationship be- tween parties involved; and victims discovered in their own home, often by the offender. Staged scenes were separated by type with staged burglaries, suicides, accidents, and car accidents examined in more detail. Each type of scene displays differently with separate indicators and common features. Features of staged burglaries were: no points of entry/exit staged; non-valuables taken; scene ransacking; offender self- injury; and offenders bringing weapons to the scene. Features of staged suicides included: weapon arrangement and simulating self-injury to the victim; rearranging the body; and removing valuables. Examples of elements of staged accidents were arranging the implement/weapon and re- positioning the deceased; while staged car accidents involved: transporting the body to the vehicle and arranging both; mutilation after death; attempts to secure an alibi; and clean up at the primary crime scene. The results suggest few staging behaviors are used, despite the credibility they may have offered the façade. This is the first peer-reviewed, published study to examine the specific features of these scenes, and is the largest sample studied to date.

Detecting common dynamics in transitory components

This paper considers VECMs for variables exhibiting cointegration and common features in the transitory components. While the presence of cointegration between the permanent components of series reduces the rank of the long-run multiplier matrix, a common feature among the transitory components leads to a rank reduction in the matrix summarizing short-run dynamics. The common feature also implies that there exists linear combinations of the first-differenced variables in a cointegrated VAR that are white noise and traditional tests focus on testing for this characteristic. An alternative, however, is to test the rank of the short-run dynamics matrix directly. Consequently, we use the literature on testing the rank of a matrix to produce some alternative test statistics. We also show that these are identical to one of the traditional tests. The performance of the different methods is illustrated in a Monte Carlo analysis which is then used to re-examine an existing empirical study. Finally, this approach is applied to provide a check for the presence of common dynamics in DSGE models.

Granule-based knowledge representation for intra and inter transaction association mining

Abstract With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules. To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.

Accurate silhouettes for surveillance : improved motion segmentation using graph cuts

Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, however such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. In this paper, the work of [3] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [3] is shown.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Multicultural education, global studies and studies of Asia

Like other nations, Australia has experienced significant change in the past few decades as its society has become increasingly diverse. The new cultures and traditions that result from ethnic and religious diversity have both enriched Australian society and presented it with some challenges. Other challenges have resulted from increased globalisation. For example, the economic fallout from the recent global financial crisis indicates that global issues can impact across a range of levels, from multinational corporations and nation-states to local sites and individual livelihoods. Some suggest that Australia fared better than other nations during this economic crisis because of its export trade with China. Although this is disputed by economists, it highlights another facet of change that is impacting on Australian society and this relates to Australia’s growing engagement with the nations of Asia. There is increasing awareness in education systems that if young people are to achieve their potential as future citizens they need to be able to negotiate the cultural, social, political and economic ties that connect them to the global and regional community through work, leisure and citizenship. Multicultural education, global studies and studies of Asia play a particular part in helping young people to: • appreciate cultural diversity within and beyond their own nation • imagine with some accuracy how others view their world • participate in shaping a better future. This chapter explores the origins, distinctions and common features of each approach.

Towards a unified method for the knowledge based urban development framework

Purpose–The growing debate in the literature indicates that the initiative to implement Knowledge Based Urban Development (KBUD) approaches in urban development process is neither simple nor quick. Many research efforts has therefore, been put forward to the development of appropriate KBUD framework and KBUD practical approaches. But this has lead to a fragmented and incoherent methodological approach. This paper outlines and compares a few most popular KBUD frameworks selected from the literature. It aims to identify some key and common features in the effort to achieve a unified method of KBUD framework. Design/methodology/approach–This paper reviews, examines and identifies various popular KBUD frameworks discussed in the literature from urban planners’ viewpoint. It employs a content analysis technique i.e. a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Originality/value–The paper reports on the key and common features of a few selected most popular KBUD frameworks. The synthesis of the results is based from a perspective of urban planners. The findings which encompass a new KBUD framework incorporating the key and common features will be valuable in setting a platform to achieve a unified method of KBUD. Practical implications –The discussion and results presented in this paper should be significant to researchers and practitioners and to any cities and countries that are aiming for KBUD. Keywords – Knowledge based urban development, Knowledge based urban development framework, Urban development and knowledge economy

Songs of Resilience

The chapters of this book form a persuasive chorus of social practices that advocate the use of music to build a capacity for resilience in individuals and groups. As a whole they exemplify music projects that share common features aligned with an ecological view of reform in health, education and social work systems. Internationally renowned and early career academics have collaborated with practitioners to sing ‘Songs of Resilience’; some of which are narratives that report on the effects of music practices for a general population, and some are based on a specific approach, genre or service. Others are quite literally ‘songs’ that demonstrate aspects of resilience in action. The book makes the connection between music and resilience explicit by posing the following questions—Do music projects in education, health and social services build a measurable capacity for resilience amongst individuals? Can we replicate these projects’ outcomes to develop a capacity for resilience in diverse cultural groups? Does shared use of the term ‘resilience’ help to secure funding for innovative musical activities that provide tangible health, education and social outcomes?

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

It’s not me, it’s you : a participant observation case study of the self-publishing musician in the 21st century

This thesis explores the business environment for self-publishing musicians at the end of the 20th century and the start of the 21st century from theoretical and empirical standpoints. The exploration begins by asking three research questions: what are the factors affecting the sustainability of an Independent music business; how many of those factors can be directly influenced by an Independent musician in the day-to-day operations of their musical enterprise; and how can those factors be best manipulated to maximise the benefit generated from digital music assets? It answers these questions by considering the nature of value in the music business in light of theories of political economy, then quantitative and qualitative examinations of the nature of participation in the music business, and then auto-ethnographic approaches to the application of two technologically enabled tools available to Independent musicians. By analyzing the results of five different examinations of the topic it answers each research question with reference to four sets of recurring issues that affect the operations of a 21st century music business: the musicians’ personal characteristics, their ability to address their business’s informational needs; their ability to manage the relationships upon which their business depends; and their ability to resolve the remaining technological problems that confront them. It discusses ways in which Independent self-publishing musicians can and cannot deal with these four issues on a day-to-day basis and highlights aspects for which technological solutions do not exist as well as ways in which technology is not as effective as has been claimed. It then presents a self-critique and proposes some directions for further study before concluding by suggesting some common features of 21st century Independent music businesses. This thesis makes three contributions to knowledge. First, it provides a new understanding of the sources of musical value, shows how this explains changes in the music industries over the past 30 years, and provides a framework for predicting future developments in those industries. Second, it shows how the technological discontinuity that has occurred around the start of the 21st century has and has not affected the production and distribution of digital cultural artefacts and thus the attitudes, approaches, and business prospects of Independent musicians. Third, it argues for new understandings of two methods by which self-publishing musicians can grow a business using production methods that are only beginning to be more broadly understood: home studio recording and fan-sourced production. Developed from the perspective of working musicians themselves, this thesis identifies four sets of issues that determine the probable success of musicians’ efforts to adopt new technologies to capture the value of the musicians’ creativity and thereby foster growth that will sustain an Independent music business in the 21st century.

Knowledge to deal with challenges to science education from without and within

This chapter focuses on two challenges to science teachers’ knowledge that Fensham identifies as having recently emerged—one a challenge from beyond Science and the other a challenge from within Science. Both challenges stem from common features of contemporary society, namely, its complexity and uncertainty. Both also confront science teachers with teaching situations that contrast markedly with the simplicity and certainty that have been characteristic of most school science education, and hence both present new demands for science teachers’ knowledge and skill. The first, the challenge from without Science, comes from the new world of work and the “knowledge society”. Regardless of their success in traditional school learning, many young persons in many modern economies are now seen as lacking other knowledge and skills that are essential for their personal, social and economic life. The second, the challenge from within Science, derives from changing notions of the nature of science itself. If the complexity and uncertainty of the knowledge society demand new understandings and contributions from science teachers, these are certainly matched by the demands that are posed by the role of complexity and uncertainty in science itself.

Analysis of ten years historical weather data in Australia

Typical reference year (TRY) weather data is often used to represent the long term weather pattern for building simulation and design. Through the analysis of ten year historical hourly weather data for seven Australian major capital cities using the frequencies procedure of descriptive statistics analysis (by SPSS software), this paper investigates: • the closeness of the typical reference year (TRY) weather data in representing the long term weather pattern; • the variations and common features that may exist between relatively hot and cold years. It is found that for the given set of input data, in comparison with the other weather elements, the discrepancy between TRY and multiple years is much smaller for the dry bulb temperature, relative humidity and global solar irradiance. The overall distribution patterns of key weather elements are also generally similar between the hot and cold years, but with some shift and/or small distortion. There is little common tendency of change between the hot and the cold years for different weather variables at different study locations.

Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach

This study proposes a full Bayes (FB) hierarchical modeling approach in traffic crash hotspot identification. The FB approach is able to account for all uncertainties associated with crash risk and various risk factors by estimating a posterior distribution of the site safety on which various ranking criteria could be based. Moreover, by use of hierarchical model specification, FB approach is able to flexibly take into account various heterogeneities of crash occurrence due to spatiotemporal effects on traffic safety. Using Singapore intersection crash data(1997-2006), an empirical evaluate was conducted to compare the proposed FB approach to the state-of-the-art approaches. Results show that the Bayesian hierarchical models with accommodation for site specific effect and serial correlation have better goodness-of-fit than non hierarchical models. Furthermore, all model-based approaches perform significantly better in safety ranking than the naive approach using raw crash count. The FB hierarchical models were found to significantly outperform the standard EB approach in correctly identifying hotspots.

Towards a comprehensive and integrated knowledge-based urban development model : status quo and directions

Incorporating knowledge based urban development (KBUD) strategies in the urban planning and development process is a challenging and complex task due to the fragmented and incoherent nature of the existing KBUD models. This paper scrutinizes and compares these KBUD models with an aim of identifying key and common features that help in developing a new comprehensive and integrated KBUD model. The features and characteristics of the existing KBUD models are determined through a thorough literature review and the analysis reveals that while these models are invaluable and useful in some cases, lack of a comprehensive perspective and absence of full integration of all necessary development domains render them incomplete as a generic model. The proposed KBUD model considers all central elements of urban development and sets an effective platform for planners and developers to achieve more holistic development outcomes. The proposed model, when developed further, has a high potential to support researchers, practitioners and particularly city and state administrations that are aiming to a knowledge-based development.

A survey of tagging techniques for music, speech and environmental sound

Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas.We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches.After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years.