Exploring assemblages of appraisal in web archives

Even after over 20 years of active web archiving we know surprising little about how archivists appraise and select web content for preservation. Since we can’t keep it all, how we decide what to keep from the web is certain to shape the historical record (Cook 2011). In this context, we ask the following research questions: 1. How are archivists deciding what to collect from the web? ; 2. How do technologies for web archiving figure in their appraisal decisions? ; 3. Are there opportunities to design more useful systems for the appraisal of content for web archives

A focus on learning : Wuality in teaching & learning : The proceedings of the Teaching & Learning Forum, Edith Cowan University, Perth, February 1995

These papers represent the proceedings of the fourth Teaching and Learning Forum conducted in Perth from February 7-9, 1995. Curtin University hosted the first two Forums and we at Edith Cowan University the third and fourth. In 1996 the honour (and the hard work) transfers to Murdoch. The Forum\u27s objectives were: • To bring together people in higher education who are interested in practical teaching issues (Lecturers, managers, administrators, students, support, general and technical staff). • To share ideas, information and practices in a variety of mutually supportive, friendly and co-operative ways. • To celebrate quality in teaching and learning and raise the status of teaching and learning in tertiary institutions. We believe that these were achieved. This set of proceedings is not organised around a set of sub themes, but rather is presented in alphabetical order with outlines of workshops and short presentations taking their place alongside research papers - as was the case at the Forum

Quality in teaching and learning: making it happen: the proceedings of the teaching & learning forum

These papers represent the proceedings of the third Teaching and Learning Forum to be conducted in Perth, the first two by Curtin University and this one by Edith Cowan University.... The Forum was organised around three major themes - or at least it was in the beginning! Papers that were presented, and the discussion that was generated, led us to concede that there was really a great deal of interaction between the themes, which were: • ways of defining quality • working towards quality • evaluating quality Accordingly, we have not attempted to designate papers as being solely or even predominantly within any one of the themes. Rather, we present them in alphabetical order with outlines of workshops and short presentations taking their place alongside research papers- as was the case at the Forum

Wavelet Analysis in Virtual Colonoscopy

The computed tomographic colonography (CTC) computer aided detection (CAD) program is a new method in development to detect colon polyps in virtual colonoscopy. While high sensitivity is consistently achieved, additional features are desired to increase specificity. In this paper, a wavelet analysis was applied to CTCCAD outputs in an attempt to filter out false positive detections. 52 CTCCAD detection images were obtained using a screen capture application. 26 of these images were real polyps, confirmed by optical colonoscopy and 26 were false positive detections. A discrete wavelet transform of each image was computed with the MATLAB wavelet toolbox using the Haar wavelet at levels 1-5 in the horizontal, vertical and diagonal directions. From the resulting wavelet coefficients at levels 1-3 for all directions, a 72 feature vector was obtained for each image, consisting of descriptive statistics such as mean, variance, skew, and kurtosis at each level and orientation, as well as error statistics based on a linear predictor of neighboring wavelet coefficients. The vectors for each of the 52 images were then run through a support vector machine (SVM) classifier using ten-fold cross-validation training to determine its efficiency in distinguishing polyps from false positives. The SVM results showed 100% sensitivity and 51% specificity in correctly identifying the status of detections. If this technique were added to the filtering process of the CTCCAD polyp detection scheme, the number of false positive results could be reduced significantly

Self-Assembly of 4-sided Fractals in the Two-handed Tile Assembly Model

We consider the self-assembly of fractals in one of the most well-studied models of tile based self-assembling systems known as the Two-handed Tile Assembly Model (2HAM). In particular, we focus our attention on a class of fractals called discrete self-similar fractals (a class of fractals that includes the discrete Sierpi\'nski carpet). We present a 2HAM system that finitely self-assembles the discrete Sierpi\'nski carpet with scale factor 1. Moreover, the 2HAM system that we give lends itself to being generalized and we describe how this system can be modified to obtain a 2HAM system that finitely self-assembles one of any fractal from an infinite set of fractals which we call 4-sided fractals. The 2HAM systems we give in this paper are the first examples of systems that finitely self-assemble discrete self-similar fractals at scale factor 1 in a purely growth model of self-assembly. Finally, we show that there exists a 3-sided fractal (which is not a tree fractal) that cannot be finitely self-assembled by any 2HAM system

Validating Pareto Optimal Operation Parameters of Polyp Detection Algorithms for CT Colonography

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or the supine scan or both. In the two-fold CV, we randomly divided the 133 patients into two cohorts. Each cohort was used to obtain the Pareto front by a multi-objective genetic algorithm, where a set of optimized thresholds was applied on the test cohort to get test results. This process was repeated twice so that each cohort was used in the training and testing process once. We averaged the two training Pareto fronts as our final training Pareto front and averaged the test results from the two runs in the CV as our final test results. Our experiments demonstrated that the averaged testing results were close to the mean Pareto front determined from the training process. We conclude that the Pareto front-based algorithm appears to be generalizable to new test data

Hybrid Committee Classifier for a Computerized Colonic Polyp Detection System

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features (numbers varied from 10-20) were selected for 11 NN classifiers which were again combined to form a NN committee classifier. Finally, a hybrid committee classifier was defined by combining the outputs of both the SVM and NN committees. The method was tested on CTC scans (supine and prone views) of 29 patients, in terms of the partial area under a free response receiving operation characteristic (FROC) curve (AUC). Our results showed that the hybrid committee classifier performed the best for the prone scans and was comparable to other classifiers for the supine scans

Prediction of Brain Tumor Progression Using Multiple Histogram Matched MRI Scans

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients\u27 MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans revealed that histograms of MRI scans such as T1, T2, FLAIR etc taken at different times have slight shifts or different shapes. This is because those MRI scans are qualitative instead of quantitative so MRI scans taken at different times or by different scanners might have slightly different scales or have different homogeneities in the scanning region. In this paper, we proposed a method to overcome this difficulty. The overall goal of this study is to assess brain tumor progression by exploring seven patients\u27 complete MRI records scanned during their visits in the past two years. There are ten MRI series in each visit, including FLAIR, T1-weighted, post-contrast T1-weighted, T2-weighted and five DTI derived MRI volumes: ADC, FA, Max, Min and Middle Eigen Values. After registering all series to the corresponding DTI scan at the first visit, we applied a histogram matching algorithm to non-DTI MRI scans to match their histograms to those of the corresponding MRI scans at the first visit. DTI derived series are quantitative and do not require the histogram matching procedure. A machine learning algorithm was then trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit B to visit C. An average of 72% pixel-wise accuracy was achieved for tumor progression prediction from visit B to visit C

Adjacent Slice Prostate Cancer Prediction to Inform MALDI Imaging Biomarker Analysis

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice