Search engine content analysis

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

Protocol engineering for protection against denial-of-service attacks

Denial-of-service attacks (DoS) and distributed denial-of-service attacks (DDoS) attempt to temporarily disrupt users or computer resources to cause service un- availability to legitimate users in the internetworking system. The most common type of DoS attack occurs when adversaries °ood a large amount of bogus data to interfere or disrupt the service on the server. The attack can be either a single-source attack, which originates at only one host, or a multi-source attack, in which multiple hosts coordinate to °ood a large number of packets to the server. Cryptographic mechanisms in authentication schemes are an example ap- proach to help the server to validate malicious tra±c. Since authentication in key establishment protocols requires the veri¯er to spend some resources before successfully detecting the bogus messages, adversaries might be able to exploit this °aw to mount an attack to overwhelm the server resources. The attacker is able to perform this kind of attack because many key establishment protocols incorporate strong authentication at the beginning phase before they can iden- tify the attacks. This is an example of DoS threats in most key establishment protocols because they have been implemented to support con¯dentiality and data integrity, but do not carefully consider other security objectives, such as availability. The main objective of this research is to design denial-of-service resistant mechanisms in key establishment protocols. In particular, we focus on the design of cryptographic protocols related to key establishment protocols that implement client puzzles to protect the server against resource exhaustion attacks. Another objective is to extend formal analysis techniques to include DoS- resistance. Basically, the formal analysis approach is used not only to analyse and verify the security of a cryptographic scheme carefully but also to help in the design stage of new protocols with a high level of security guarantee. In this research, we focus on an analysis technique of Meadows' cost-based framework, and we implement DoS-resistant model using Coloured Petri Nets. Meadows' cost-based framework is directly proposed to assess denial-of-service vulnerabil- ities in the cryptographic protocols using mathematical proof, while Coloured Petri Nets is used to model and verify the communication protocols using inter- active simulations. In addition, Coloured Petri Nets are able to help the protocol designer to clarify and reduce some inconsistency of the protocol speci¯cation. Therefore, the second objective of this research is to explore vulnerabilities in existing DoS-resistant protocols, as well as extend a formal analysis approach to our new framework for improving DoS-resistance and evaluating the performance of the new proposed mechanism. In summary, the speci¯c outcomes of this research include following results; 1. A taxonomy of denial-of-service resistant strategies and techniques used in key establishment protocols; 2. A critical analysis of existing DoS-resistant key exchange and key estab- lishment protocols; 3. An implementation of Meadows's cost-based framework using Coloured Petri Nets for modelling and evaluating DoS-resistant protocols; and 4. A development of new e±cient and practical DoS-resistant mechanisms to improve the resistance to denial-of-service attacks in key establishment protocols.

What do clients learn from counseling?

Thirty-five clients who received counseling participated in this exploratory study by completing a letter to a friend that described in as much detail as possible what they had learned from counseling. The participants’ written responses were analyzed using a content analysis approach. The analysis indicated that the data were best categorized in terms of three broad areas of learnings (Self, Relations with Others, and the Process of Learning and Change). The Self taxonomy was found to consist of six hierarchical levels. The Relations with Others taxonomy consisted of five hierarchical levels, while the Process of Learning and Change taxonomy consisted of five hierarchical levels. The results suggested that these three taxonomies offer a promising and exciting way to view the impact of counseling within a learning framework. If these taxonomies are found to be stable in future research and clients are easily classified using the taxonomies then this approach may have implications for counseling. It may well be that to maximise the learnings counselors could use specific strategies and techniques to enhance their clients’ learning in the three areas.

Location constraints in digital rights management

Digital rights management allows information owners to control the use and dissemination of electronic documents via a machine-readable licence. This paper describes the design and implementation of a system for creating and enforcing licences containing location constraints that can be used to restrict access to sensitive documents to a defined area. Documents can be loaded onto a portable device and used in the approved areas, but cannot be used if the device moves to another area. Our contribution includes a taxonomy for access control in the presence of requests to perform non-instantaneous controlled actions.

A study of entrepreneurship : Taiwanese digital content companies in China

China’s accession to the World Trade Organisation (WTO) has greatly enhanced global interest in investment in the Chinese media market, where demand for digital content is growing rapidly. The East Asian region is positioned as a growth area in many forms of digital content and digital service industries. China is attempting to catch up and take its place as a production centre to offset challenges from neighbouring countries. Meanwhile, Taiwan is seeking to use China both as an export market and as a production site for its digital content. This research investigates entry strategies of Taiwanese digital content firms into the Chinese market. By examining the strategies of a sample of Taiwan-based companies, this study also explores the evolution of their market strategies. However, the focus is on how distinctive business practices such as guanxi are important to Taiwanese business and to relations with Mainland China. This research examines how entrepreneurs manage the characteristics of digital content products and in turn how digital content entrepreneurs adapt to changing market circumstances. This project selected five Taiwan-based digital content companies that have business operations in China: Wang Film, Artkey, CnYES, Somode and iPartment. The study involved a field trip, undertaken between November 2006 and March 2007 to Shanghai and Taiwan to conduct interviews and to gather documentation and archival reports. Six senior managers and nine experts were interviewed. Data were analysed according to Miller’s firm-level entrepreneurship theory, foreign direct investment theory, Life Cycle Model and guanxi philosophy. Most studies of SMEs have focused on free market (capitalist) environments. In contrast, this thesis examines how Taiwanese digital content firms’ strategies apply in the Chinese market. I identified three main types of business strategy: cost-reduction, innovation and quality-enhancement; and four categories of functional strategies: product, marketing, resource acquisition and organizational restructuring. In this study, I introduce the concept of ‘entrepreneurial guanxi’, special relationships that imply mutual obligation, assurance and understanding to secure and exchange favors in entrepreneurial activities. While guanxi is a feature of many studies of business in Pan-Chinese society, it plays an important mediating role in digital content industries. In this thesis, I integrate the ‘Life Cycle Model’ with the dynamic concept of strategy. I outline the significant differences in the evolution of strategy between two types of digital content companies: off-line firms (Wang Film and Artkey) and web-based firms (CnYES, Somode and iPartment). Off-line digital content firms tended to adopt ‘resource acquisition strategies’ in their initial stages and ‘marketing strategies’ in second and subsequent stages. In contrast, web-based digital content companies mainly adopted product and marketing strategies in the early stages, and would adopt innovative approaches towards product and marketing strategies in the whole process of their business development. Some web-based digital content companies also adopted organizational restructuring strategies in the final stage. Finally, I propose the ‘Taxonomy Matrix of Entrepreneurial Strategies’ to emphasise the two dimensions of this matrix: innovation, and the firm’s resource acquisition for entrepreneurial strategy. This matrix is divided into four cells: Effective, Bounded, Conservative, and Impoverished.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Passengers in the airport : artefacts and activities

This study addresses the ordinary activities of passengers in airports. Using observational techniques we investigated how passenger activities are mediated by artefacts, in this the bags that people carry. The relationship between passengers and their bags is shown to be complex and contingent on many factors. We report on our early research in the airport and document an emerging taxonomy of passenger activity. The significance of this research is in the contribution made to an understanding of passenger activities which could contribute to the design of future technologies for passenger facilitation and to airport terminal design.

An evaluation of current and future botnet defenses

Botnets are large networks of compromised machines under the control of a bot master. These botnets constantly evolve their defences to allow the continuation of their malicious activities. The constant development of new botnet mitigation strategies and their subsequent defensive countermeasures has lead to a technological arms race, one which the bot masters have significant incentives to win. This dissertation analyzes the current and future states of the botnet arms race by introducing a taxonomy of botnet defences and a simulation framework for evaluating botnet techniques. The taxonomy covers current botnet techniques and highlights possible future techniques for further analysis under the simulation framework. This framework allows the evaluation of the effect techniques such as reputation systems and proof of work schemes have on the resources required to disable a peer-to-peer botnet. Given the increase in the resources required, our results suggest that the prospects of eliminating the botnet threat are limited.

On democracy

If one clear argument emerged from my doctoral thesis in political science, it is that there is no agreement as to what democracy is. There are over 40 different varieties of democracy ranging from those in the mainstream with subtle or minute differences to those playing by themselves in the corner. And many of these various types of democracy are very well argued, empirically supported, and highly relevant to certain polities. The irony is that the thing which all of these democratic varieties or the ‘basic democracy’ that all other forms of democracy stem from, is elusive. There is no international agreement in the literature or in political practice as to what ‘basic democracy’ is and that is problematic as many of us use the word ‘democracy’ every day and it is a concept of tremendous importance internationally. I am still uncertain as to why this problem has not been resolved before by far greater minds than my own, and it may have something to do with the recent growth in democratic theory this past decade and the innovative areas of thought my thesis required, but I think I’ve got the answer. By listing each type of democracy and filling the column next to this list with the literature associated with these various styles of democracy, I amassed a large and comprehensive body of textual data. My research intended to find out what these various styles of democracy had in common and to create a taxonomy (like the ‘tree of life’ in biology) of democracy to attempt at showing how various styles of democracy have ‘evolved’ over the past 5000 years.ii I then ran a word frequency analysis program or a piece of software that counts the 100 most commonly used words in the texts. This is where my logic came in as I had to make sense of these words. How did they answer what the most fundamental commonalities are between 40 different styles of democracy? I used a grounded theory analysis which required that I argue my way through these words to form a ‘theory’ or plausible explanation as to why these particular words and not others are the important ones for answering the question. It came down to the argument that all 40 styles of democracy analysed have the following in common 1) A concept of a citizenry. 2) A concept of sovereignty. 3) A concept of equality. 4) A concept of law. 5) A concept of communication. 6) And a concept of selecting officials. Thus, democracy is a defined citizenry with its own concept of sovereignty which it exercises through the institutions which support the citizenry’s understandings of equality, law, communication, and the selection of officials. Once any of these 6 concepts are defined in a particular way it creates a style of democracy. From this, we can also see that there can be more than one style of democracy active in a particular government as a citizenry is composed of many different aggregates with their own understandings of the six concepts.