Towards a Framework for Capturing and Distributing Rich Interactive Human Digital Memories

The area of human digital memories has placed considerable focus on documenting the things we do, the places we visit and the thoughts we think. Rather than sharing important events face–to–face, i.e. by watching home videos together or looking through photo albums, people tend to share their memories with each other through emails or text messages, or by posting them online. The difficulty is that the vast amounts of data we collect are often difficult to access and less meaningful to us over time. The challenge is to structure human digital memories in a way that can be easily distributed and recollected at different time periods in our lives. More specifically, the collection and organisation of memory-related information (images, video, physiological data and so on) needs to occur using ubiquitous ad hoc services, prevalent within the environments we occupy. This is likely to happen without us necessarily being aware that memories are being created. This will remove the need to manage the growing number of information sources that require conventional tools to achieve this, for example, a camera to take stills and video. This paper posits a new and novel idea that builds on the nomadic nature of people, ubiquitous computing, context awareness, physiological computing, semantic annotation and ad hoc networking that will allow rich interactive digital memories to be created amongst individuals and their environments that are unobtrusive to individuals

Ontology-based web annotation framework for hyperlink structures

Heterogeneous and autonomous web sites contain information that cannot be easily processed by automated tools. To address the heterogeneity issues, one attempt is to add metadata to Web pages to provide semantic description of the web pages and other kinds of web objects. This is also known as web annotation. Ontology-based Web annotation (OWA) is a kind of web annotation that associates ontologies with annotations. Since each ontology is usually adopted by some user community, OWA therefore facilitates sharing of knowledge among users sharing the ontology. In our survey, we noted that most existing OWA approaches only annotate web pages, but not the hyperlinks among them.Published versio

Towards automatic traffic classification and estimation for available bandwidth in IP networks.

Growing rapidly, today's Internet is becoming more difficult to manage. A good understanding of what kind of network traffic classes are consuming network resource as well as how much network resource is available is important for many management tasks like QoS provisioning and traffic engineering. In the light of these objectives, two measurement mechanisms have been explored in this thesis. This thesis explores a new type of traffic classification scheme with automatic and accurate identification capability. First of all, the novel concept of IP flow profile, a unique identifier to the associated traffic class, has been proposed and the relevant model using five IP header based contexts has been presented. Then, this thesis shows that the key statistical features of each context, in the IP flow profile, follows a Gaussian distribution and explores how to use Kohonen Neural Network (KNN) for the purpose of automatically producing IP flow profile map. In order to improve the classification accuracy, this thesis investigates and evaluates the use of PCA for feature selection, which enables the produced patterns to be as tight as possible since tight patterns lead to less overlaps among patterns. In addition, the use of Linear Discriminant Analysis and alternative KNN maps has been investigated as to deal with the overlap issue between produced patterns. The entirety of this process represents a novel addition to the quest for automatic traffic classification in IP networks. This thesis also develops a fast available bandwidth measurement scheme. It firstly addresses the dynamic problem for the one way delay (OWD) trend detection. To deal with this issue, a novel model - asymptotic OWD Comparison (AOC) model for the OWD trend detection has been proposed. Then, three statistical metrics SOT (Sum of Trend), PTC (Positive Trend Checking) and CTC (Complete Trend Comparison) have been proposed to develop the AOC algorithms. To validate the proposed AOC model, an avail-bw estimation tool called Pathpair has been developed and evaluated in the Planetlah environment