Qualitative Methods and the Evaluation of Community Renewal Programs in Australia: Towards a National Framework

The emergence of public housing estate renewal programs in Australia in the last decade has been one of the most prominent developments in social housing policy. These programs have undertaken a broad mix of renewal activity, ranging from outright physical redevelopment and stock replacement for sale, to community development type initiatives to improve social and employment outcomes for residents. However, while a number of evaluations of these programs have been undertaken, the development of evaluation methodology has lagged behind that of other countries. This in part is due to the lack of federal government interest or involvement in these programs which are essentially state specific. The article reviews the evaluations that have been undertaken in the last 10 years in Australia and assesses the relative importance of qualitative methodologies in these evaluations. Despite a strong focus among policy makers on value-for-money aspects of renewal, the authors show that qualitative methods have been commonplace, if limited in range, and argue this is a result of both the difficulty of obtaining comparative quantitative information especially when comparing dissimilar programs between states. In this context, qualitative methods are more easily managed by researchers and offer more insightful assessments than quantitatively based approaches. The article concludes by arguing for a national evaluation methodology to assist in more rigorous evaluations and the extension of qualitative evaluation methods.     

扫描技术继续生存

我们有如此多的信息是以打印件存储的。即使电子备份业已存在，我们要面对的仍然不止是电子文件而已，IT人说要实现磁带存储太难了，电力公司不会以简便的电子格式把帐单发给你。我喜欢一切都是电子版的，但情况不会如此。与此同时，我们看看如何使现有的扫描技术为你所用。     

高性能双无源混频器可应对5G MIMO接收器挑战

5G的带宽至少需要从目前的20MHz带宽增大到100MHz甚至更高,这就意味着需要进入3.6GHz以上或更高的频段.为了满足这种需求,凌力尔特的LTC5593双无源下变频混频器在3.6GHz提供了出色的线性度和动态范围性能,同时支持超过200MHz的平坦信号带宽,可用来构成极其坚固的MIMO(多输入多输出)接收器.     

Robust hierarchical multiple hypothesis tracker for multiple-object tracking

Multiple object tracking is a fundamental subsystem of many higher level applications such as traffic monitoring, people counting, robotic vision and many more. This paper explains in details the methodology of building a robust hierarchical multiple hypothesis tracker for tracking multiple objects in the videos. The main novelties of our approach are anchor-based track initialization, prediction assistance for unconfirmed track and two virtual measurements for confirmed track. The system is built mainly to deal with the problems of merge, split, fragments and occlusion. The system is divided into two levels where the first level obtains the measurement input from foreground segmentation and clustered optical flow. Only K-best hypothesis and one-to-one association are considered. Two more virtual measurements are constructed to help track retention rate for the second level, which are based on predicted state and division of occluded foreground segments. Track based K-best hypothesis with multiple associations are considered for more comprehensive observation assignment. Histogram intersection testing is performed to limit the tracker bounding box expansion. Simulation results show that all our algorithms perform well in the surroundings mentioned above. Two performance metrics are used; multiple-object tracking accuracy (MOTA) and multiple-object tracking precision (MOTP). Our tracker have performed the best compared to the benchmark trackers in both performance evaluation metrics. The main weakness of our algorithms is the heavy processing requirement.     

Networking,Storytelling and Knowledge Production in First-Year Writing

As writing teachers, we seek to validate the knowledge that students bring to the classroom while introducing and cultivating new methods, strategies, techniques and technologies that may serve them now and in the future. In this article we describe an approach we take that integrates curricular and extracurricular co-apprenticeship strategies using public writing models in conjunction with social networking tools. With publication as a final goal, we urge students to move from expressing personal problems to addressing social issues, using a private, non-commercial learning network NoDiff <http://NoDiff.com> as a safe zone for skills development, knowledge production and social inquiry. In taking this approach, we resist dichotomies that place professional skills training in strict opposition to critical pedagogy. We argue that many of the so-called “extracurricular” composing activities that most of today's students engage in lay the groundwork for both professional and civic participation. Acknowledging that we as composition teachers are also beginners in the world of 21^st century communication, we emphasize both (1) the continual transference of skills and knowledge among teachers and learners; and (2) the importance of providing what is often lacking in skills-based ‘new literacy’ training, namely a critical pedagogical approach to learning as social engagement and critical intervention.     

An Integrated Approach to High Integrity Software Verification

Using automated reasoning techniques, we tackle the niche activity of proving that a program is free from run-time exceptions. Such a property is particularly valuable in high integrity software, for example, safety- or security-critical applications. The context for our work is the SPARK Approach for the development of high integrity software. The SPARK Approach provides a significant degree of automation in proving exception freedom. Where this automation fails, however, the programmer is burdened with the task of interactively constructing a proof and possibly also having to supply auxiliary program annotations. We minimize this burden by increasing the automation, through an integration of proof planning and a program analysis oracle. We advocate a ‘cooperative’ integration, where proof-failure analysis directly constrains the search for auxiliary program annotations. The approach has been successfully tested on industrial data.     

IT professional identity: needs,perceptions, and belonging

A theoretical model of professional identification is developed and empirically examined as a means to understanding information technology (IT) workers’ attachment to the IT profession. Professional identification represents oneness with or belonging to a profession and provides a unique means of investigating and evaluating the IT profession. Results from a survey of 305 IT workers indicate that professional identification is directly impacted by three factors: (1) the individual's need for professional identification; (2) the individual's perceived similarity to others in the IT profession; and (3) the individual's perceptions of the IT profession, signifying the importance of internalization to identification. Professional identification is also indirectly impacted by the public's perception of the IT profession.     

The HaLoop approach to large-scale iterative data analysis

The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce has enjoyed particular success. However, MapReduce lacks built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, and model fitting. This paper (This is an extended version of the VLDB 2010 paper “HaLoop: Efficient Iterative Data Processing on Large Clusters” PVLDB 3(1):285–296, 2010.) presents HaLoop, a modified version of the Hadoop MapReduce framework, that is designed to serve these applications. HaLoop allows iterative applications to be assembled from existing Hadoop programs without modification, and significantly improves their efficiency by providing inter-iteration caching mechanisms and a loop-aware scheduler to exploit these caches. HaLoop retains the fault-tolerance properties of MapReduce through automatic cache recovery and task re-execution. We evaluated HaLoop on a variety of real applications and real datasets. Compared with Hadoop, on average, HaLoop improved runtimes by a factor of 1.85 and shuffled only 4 % as much data between mappers and reducers in the applications that we tested.