Message Integrity in the World Wide Web: Use of Nested Hash Function and a Fast Stream Cipher

The focus of this work is to provide authentication and confidentiality of messages in a swift and cost effective manner to suit the fast growing Internet applications. A nested hash function with lower computational and storage demands is designed with a view to providing authentication as also to encrypt the message as well as the hash code using a fast stream cipher MAJE4 with a variable key size of 128-bit or 256-bit for achieving confidentiality. Both nested Hash function and MAJE4 stream cipher algorithm use primitive computational operators commonly found in microprocessors; this makes the method simple and fast to implement both in hardware and software. Since the memory requirement is less, it can be used for handheld devices for security purposes.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Academic integrity guidelines from Dialog Plus

The Academic Integrity Guidelines (AIG), initally designed at Penn State University, is a small nugget from the JISC Project Dialog Plus which tests students' understanding of good academic integrity. The nugget presents students with an overview of their own institution's integrity guidelines before moving onto completing a set of multiple choice questions. This particular nugget has proved so popular that it has been exported from PSU and embedded into courses at both Leeds and Southampton.

Academic integrity tutorial

This tutorial presents what the School of Electronics and Computer Science expects of its undergraduate students.

Academic Integrity Guidelines: School of Geography's Policy

The School of Geography's policy and requirements in dealing with matters related to academic integrity and plagiarism.

Academic Integrity Guidelines - Quiz and References

A multiple-choice quiz to test a student's understanding of academic integrity and plagiarism. A range of quiz formats including Blackboard, IMS-QTI, plain text and Respondus is available for users to download.

Academic Integrity Guidelines - Professional Values

Explanatory notes on why academic integrity is important for students in their professional and personal development.

Academic integrity collection

This Collection includes a range of resources all of which cover the University policy on academic integrity.

Academic integrity at the University of Southampton

This is a two part audio podcast designed to provide advice and guidance for students at the University of Southampton. Transcripts for each part of the podcast are also provided.

Academic integrity resources at the University of Southampton

These are resources within EdShare which provide support and guidance to students on working with academic integrity and avoiding plagiarism.

Academic Integrity Yes or No - Questions Only

A set of questions to be used in class to introduce the concept of academic integrity.

Academic Integrity

Understanding what is meant by plagiarism. How to ensure that you are not accused of plagiarism or cheating. How to properly reference material you copy.

Academic Integrity

PowerPoint slides for talk on Academic Integrity. Examples are taken from the Medical Literature. Reference is made to the University's Academic Integrity Statement. A quiz using Zappers is included in the presentation.