COMP3016 Web Technology - Strand 3 "History" Lecture 2

Lecture 1: Contributions of Pre WWW Research: Open Hypermedia Systems Contains Powerpoint Lecture slides and Hypertext Research Papers: Industrial Strength Hypermedia: Requirements for a Large Engineering Enterprise (Malcolm et al. 1991); Towards An Integrated Information Environment With Open Hypermedia Systems (Davis et al. 1992); Unifying Strategies for Web Augmentation (Bouvin 1999); Hyper-G (Adapted from Lowe and Hall); OHP:A Draft Proposal for a Standard Open Hypermedia Protocol (Davis et al. 1996); XML Linking (DeRose 99)

COMP3016 Web Technology - Strand 3 "History" Lecture 3

Lecture 3: Contributions of Pre WWW Research: Spatial Hypertext and Temporal Hypertext Contains Powerpoint Lecture slides and Hypertext Research Papers: Spatial [SPATIAL] VIKI: spatial hypertext supporting emergent structure (Marshall, 94); Towards Geo-Spatial Hypermedia: Concepts and Prototype Implementation, (Gronbaek et al. 2002); Cyber Geography and Better Search Engines; [TEMPORAL] Anticipating SMIL 2.0: The Developing Cooperative Infrastructure for Multimedia on the Web (Rutledge 1999); Its About Time: Link Streams as Continuous Metadata (Page et al., 2001); Everything You Wanted to Know About MPEG-7:Part 1 (Nack & Lindsay 1999)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 6: Network Evolution and Process

In this class, we will discuss the nature of network evolution and some selected network processes. We will discuss graph generation algorithms that generate networks with different interesting characteristics. Optional : The Structure and Function of Complex Networks (chapter 8), M.E.J. Newman, SIAM Review 45 167--256 (2003); Optional: Emergence of Scaling in Random Networks, A.L. Barabasi and R. Albert, Science 286, 509 (1999)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 3: Network Theory and Terminology

In this class, we will discuss network theory fundamentals, including concepts such as diameter, distance, clustering coefficient and others. We will also discuss different types of networks, such as scale-free networks, random networks etc. Readings: Graph structure in the Web, A. Broder and R. Kumar and F. Maghoul and P. Raghavan and S. Rajagopalan and R. Stata and A. Tomkins and J. Wiener Computer Networks 33 309--320 (2000) [Web link, Alternative Link] Optional: The Structure and Function of Complex Networks, M.E.J. Newman, SIAM Review 45 167--256 (2003) [Web link] Original course at: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 1

In this class, we will discuss the course organization and provide a basic motivation for and introduction to the course. Readings: Web science: a provocative invitation to computer science, B. Shneiderman, Communications of the ACM 50 25--27 (2007) [Web link] Readings: Chapter 1 & 2, A Framework for Web Science, T. Berners-Lee and W. Hall and J. A. Hendler and K. O'Hara and N. Shadbolt and D. J. Weitzner Foundations and Trends® in Web Science 1 (2006) [Web link] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 4: Social Network Analysis

What are fundamental entities in social networks and what information is contained in social graphs? We will discuss some selected concepts in social network analysis, such as one- and two mode networks, prestige and centrality, and cliques, clans and clubs. Readings: Web tool predicts election results and stock prices, J. Palmer, New Scientist, 07 February (2008) [Protected Access] Optional: Social Network Analysis, Methods and Applications, S. Wasserman and K. Faust (1994)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 8: Web Technologies 1

This class focuses on a selected subset of web technologies that are of interest to the topics of this course. Readings: Chapter 5 "Representational State Transfer (REST)", in "Architectural Styles and the Design of Network-based Software Architecture", Roy Fielding, Dissertation, University of California Irvine, 2000 Optional: Chapter "Representational State Transfer (REST)" in "Pro PHP XML and Web Services", R. Richards 633--672, 2006

Automated pre-processing strategies for species occurrence data used in biodiversity modelling

To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

Instance generators and test suites for the multiobjective quadratic assignment problem

We describe, and make publicly available, two problem instance generators for a multiobjective version of the well-known quadratic assignment problem (QAP). The generators allow a number of instance parameters to be set, including those controlling epistasis and inter-objective correlations. Based on these generators, several initial test suites are provided and described. For each test instance we measure some global properties and, for the smallest ones, make some initial observations of the Pareto optimal sets/fronts. Our purpose in providing these tools is to facilitate the ongoing study of problem structure in multiobjective (combinatorial) optimization, and its effects on search landscape and algorithm performance.

Implementation of filters for image pre-processing for leaf analyses in plantations

In this work an image pre-processing module has been developed to extract quantitative information from plantation images with various degrees of infestation. Four filters comprise this module: the first one acts on smoothness of the image, the second one removes image background enhancing plants leaves, the third filter removes isolated dots not removed by the previous filter, and the fourth one is used to highlight leaves' edges. At first the filters were tested with MATLAB, for a quick visual feedback of the filters' behavior. Then the filters were implemented in the C programming language. At last, the module as been coded in VHDL for the implementation on a Stratix II family FPGA. Tests were run and the results are shown in this paper. © 2008 Springer-Verlag Berlin Heidelberg.

Interrelation of peri-operative morbidity and ASA class assignment in patients undergoing gynaecological surgery

OBJECTIVE: The aim of this study was to estimate intra- and post-operative risk using the American Society of Anaesthesiologists (ASA) classification which is an important predictor of an intervention and of the entire operating programme. STUDY DESIGN: In this retrospective study, 4435 consecutive patients undergoing elective and emergency surgery at the Gynaecological Clinic of the University Hospital of Zurich were included. The ASA classification for pre-operative risk assessment was determined by an anaesthesiologist after a thorough physical examination. We observed several pre-, intra- and post-operative parameters, such as age, body-mass-index, duration of anaesthesia, duration of surgery, blood loss, duration of post-operative stay, complicated post-operative course, morbidity and mortality. The investigation of different risk factors was achieved by a multiple linear regression model for log-transformed duration of hospitalisation. RESULTS: Age and obesity were responsible for a higher ASA classification. ASA grade correlates with the duration of anaesthesia and the duration of the surgery itself. There was a significant difference in blood loss between ASA grades I (113+/-195 ml) and III (222+/-470 ml) and between classes II (176+/-432 ml) and III. The duration of post-operative hospitalisation could also be correlated with ASA class. ASA class I=1.7+/-3.0 days, ASA class II=3.6+/-4.3 days, ASA class III=6.8+/-8.2 days, and ASA class IV=6.2+/-3.9 days. The mean post-operative in-hospital stay was 2.5+/-4.0 days without complications, and 8.7+/-6.7 days with post-operative complications. Multiple linear regression model showed that not only the ASA classification contained an important information for the duration of hospitalisation. Parameters such as age, class of diagnosis, post-operative complications, etc. also have an influence on the duration of hospitalisation. CONCLUSION: This study shows that the ASA classification can be used as a good and early available predictor for the planning of an intervention in gynaecological surgery. The ASA classification helps the surgeon to assess the peri-operative risk profile of which important information can be derived for the planning of the operation programme.

Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

HNET/UROAD lecture guide. Final report.

Federal Highway Administration, Planning Analysis Division, Washington, D.C.

Sermons preached before the University of Oxford : in the year MDCCCVI, at the lecture founded by the Rev. John Bampton, M.A., late Canon of Salisbury /

NUC pre-1956 number of original : NB 0872227