No description available.

This thesis describes a formal model of a subtype of humour, and the implementation of that model in a program that generates jokes of that subtype. Although there is a great deal of literature on humour in general, very little formal work has been done on puns, and none has been implemented. All current linguistic theories of humour are over-general and not falsifiable. Our model, which is specific, formal, implemented and evaluated, makes a significant contribution to the field. Punning riddles are our chosen subtype of verbal humour, for several reasons. They are very common, they exhibit certain regular structures and mechanisms, and they have been studied previously by linguists. Our model is based on our extensive analysis of large numbers of punning riddles, taken from children's joke books. The implementation of the model, JAPE (Joke Analysis and Production Engine), generates punning riddles, from a humour independent lexicon. Pun generation requires much less world knowledge than pun comprehension, making it feasible for implementation. To support our claim that all of JAPE's output is punning riddles, we conducted an evaluatory experiment. We took JAPE texts, human-generated texts, nonsense non-jokes and sensible non-jokes, and asked joke experts to evaluate them. For joke experts, we used 8-11 year old children, since psychological research suggests that this age group enjoys, and can recognize, punning riddles better than other age groups. The results showed that JAPE's output texts are, in fact, recognizably jokes. The evaluation showed that our model adequately describes a significant subtype of verbal humour. We believe that this model can now be expanded to cover puns in general, as well as other types of linguistic humour.

This dissertation explores how decisions about the forecasting process can affect the evaluation of forecasting performance, in general and in the domain of residential electricity demand estimation. Decisions of interest include those around data sourcing, sampling, clustering, temporal magnification, algorithm selection, testing approach, evaluation metrics, and others. Models of the forecasting process and analysis methods are formulated in terms of a three-tier decision taxonomy, by which decision effects are exposed through systematic enumeration of the techniques resulting from those decisions. A computation platform based on the models is implemented to compute and visualize the effects. The methods and computation platform are first demonstrated by applying them to 3,003 benchmark datasets to investigate various decisions, including those that could impact the relationship between data entropy and forecastability. Then, they are used to study over 10,624 week-ahead and day-ahead residential electricity demand forecasting techniques, utilizing fine-resolution electricity usage data collected over 18 months on groups of 782 and 223 households by real smart electric grids in Ireland and Australia, respectively. The main finding from this research is that forecasting performance is highly sensitive to the interaction effects of many decisions. Sampling is found to be an especially effective data strategy, clustering not so, temporal magnification mixed. Other relationships between certain decisions and performance are surfaced, too. While these findings are empirical and specific to one practically scoped investigation, they are potentially generalizable, with implications for residential electricity demand estimation, smart electric grid design, and electricity policy.

No description available.

Les délais auto-inflammations des divers mélanges de carburants (méthane, gaz naturel, gaz de synthèse) en phase gazeuse aux températures basses et intermédiaires (800 à 1010 K) et hautes pressions (0,5 à 2,5 MPa) ont été mesurés dans la Machine à Compression Rapide (MCR) de l’Université de Lille 1. Différentes quantités d’hydrogène ou d’additifs représentant une composition-type d’EGR (CO, CO2, H2O) ont été ajoutées au gaz naturel pour étudier leur effet sur les délais d’auto-inflammation. L’effet des conditions opératoires (la pression et la température) et l’effet de la richesse des mélanges ont été également étudiés. Le mécanisme GDF-kin® 4 développé par GDF-SUEZ a été utilisé pour modéliser les résultats expérimentaux. Ce mécanisme a été amélioré pour reproduire les délais d'auto-inflammation dans nos conditions d'étude. Le nouveau mécanisme a également été validé à l'aide de nombreux résultats expérimentaux de la littérature. / The ignition delay of various gaseous fuel (methane, natural gas, syngas) at low and intermediate temperatures (800 to 1010K) and high pressure (0,5 to 2,5 MPa) were measured in the rapid compression machine of the University of Lille 1. Different amounts of hydrogen or additives representing a composition type EGR (CO, CO2, H2O) were added to natural gas in ordre to study their influence on the ignition delay. The effect of operating conditions (pressure and temperature) and the equivalence radio of the fuel were also studied. The mechanism GDF-kin® 4 developed by GDF SUEZ has been used to model the experimental results. This mechanism has been improved to reproduce the ignition delay in our conditon. The new mechanism has also been validated with various experimental results from the literatures.

The work, of which this thesis is a record, is concerned with the development of a microprocessor-based signal analyser for machine condition monitoring. Until recently, the technology did not exist to produce such an instrument in a 'semi-portable' form. The work proceeds with a revision of condition monitoring and digital techniques which may be implemented on such an instrument. These techniques can be used to detect such faults as: bearing wear; out of balance; shaft misalignment; damaged gears and electrically induced vibration. The greater part of this work involves: firstly, establishing a suitable hardware architecture for the instrument and, secondly, implementing the digital signal processing algorithms required. Such a system, capable of implementing both time and spectral techniques, has been developed to laboratory prototype level. The system consists of a high-speed, multi-channel data acquisition unit and both 8-bit and 16-bit microprocessor systems. The microprocessors execute the operating system and signal processing software. The aim, to produce a simple to use and flexible instrument, was sustained throughout the design phase. The result is an instrument which should offer multi-functional protection of plant.

University of Macau / Faculty of Arts and Humanities. / Department of English

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

In “phishing attacks”, phishing websites mimic trustworthy websites in order to steal sensitive information from end-users. Despite research by both academia and the industry focusing on development of anti-phishing detection techniques, phishing has increasingly become an online threat. Our inability to slow down phishing attacks shows that we need to go beyond detection and focus more on understanding the phishing ecosystem. In this thesis, we contribute in three ways to understand the phishing ecosystem and to offer insight for future anti-phishing efforts. First, we provide a new and comparative study on the life cycle of phishing and malware attacks. Specifically, we use public click-through statistics of the Bitly URL shortening service to analyze the click-through rate and timespan of phishing and malware attacks before (and after) they were reported. We find that the efforts against phishing attacks are stronger than those against malware attacks.We also find phishing activity indicating that mitigation strategies are not taking down phishing websites fast enough. Second, we develop a method that finds similarities between the DOMs of phishing attacks, since it is known that phishing attacks are variations of previous attacks. We find that existing methods do not capture the structure of the DOM, and question whether they are failing to catch some of the similar attacks. We accordingly evaluate the feasibility of applying Pawlik and Augsten’s recent implementation of Tree Edit Distance (AP-TED)calculations as a way to compare DOMs and identify similar phishing attack instances.Our method agrees with existing ones that 94% of our phishing database are replicas. It also better discriminates the similarities, but at a higher computational cost. The high agreement between methods strengthens the understanding that most phishing attacks are variations, which affects future anti-phishing strategies.Third, we develop a domain classifier exploiting the history and internet presence of a domain with machine learning techniques. It uses only publicly available information to determine whether a known phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered. This is especially relevant due to the recent adoption of the General Data Protection Regulation (GDPR), which prevents certain registration information to be made publicly available. Our classifier achieves 94% accuracy on future malicious domains,while maintaining 88% and 92% accuracy on malicious and compromised datasets respectively from two other sources. Accurate domain classification offers insight with regard to different take-down strategies, and with regard to registrars’ prevention of fraudulent registrations.

Keung Chi-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 128-135). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Classification --- p.2 / Chapter 1.2 --- Instance-Based Learning --- p.4 / Chapter 1.2.1 --- Three Basic Components --- p.5 / Chapter 1.2.2 --- Advantages --- p.6 / Chapter 1.2.3 --- Disadvantages --- p.7 / Chapter 1.3 --- Thesis Contributions --- p.7 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Improving Instance-Based Learning --- p.10 / Chapter 2.1.1 --- Scaling-up Nearest Neighbor Searching --- p.11 / Chapter 2.1.2 --- Data Reduction --- p.12 / Chapter 2.2 --- Prototype Learning --- p.12 / Chapter 2.2.1 --- Objectives --- p.13 / Chapter 2.2.2 --- Two Types of Prototype Learning --- p.15 / Chapter 2.3 --- Instance-Filtering Methods --- p.15 / Chapter 2.3.1 --- Retaining Border Instances --- p.16 / Chapter 2.3.2 --- Removing Border Instances --- p.21 / Chapter 2.3.3 --- Retaining Center Instances --- p.22 / Chapter 2.3.4 --- Advantages --- p.23 / Chapter 2.3.5 --- Disadvantages --- p.24 / Chapter 2.4 --- Instance-Abstraction Methods --- p.25 / Chapter 2.4.1 --- Advantages --- p.30 / Chapter 2.4.2 --- Disadvantages --- p.30 / Chapter 2.5 --- Other Methods --- p.32 / Chapter 2.6 --- Summary --- p.34 / Chapter 3 --- Integration of Filtering and Abstraction --- p.36 / Chapter 3.1 --- Incremental Integration --- p.37 / Chapter 3.1.1 --- Motivation --- p.37 / Chapter 3.1.2 --- The Integration Method --- p.40 / Chapter 3.1.3 --- Issues --- p.41 / Chapter 3.2 --- Concept Integration --- p.42 / Chapter 3.2.1 --- Motivation --- p.43 / Chapter 3.2.2 --- The Integration Method --- p.44 / Chapter 3.2.3 --- Issues --- p.45 / Chapter 3.3 --- Difference between Integration Methods and Composite Clas- sifiers --- p.48 / Chapter 4 --- The PGF Framework --- p.49 / Chapter 4.1 --- The PGF1 Algorithm --- p.50 / Chapter 4.1.1 --- Instance-Filtering Component --- p.51 / Chapter 4.1.2 --- Instance-Abstraction Component --- p.52 / Chapter 4.2 --- The PGF2 Algorithm --- p.56 / Chapter 4.3 --- Empirical Analysis --- p.57 / Chapter 4.3.1 --- Experimental Setup --- p.57 / Chapter 4.3.2 --- Results of PGF Algorithms --- p.59 / Chapter 4.3.3 --- Analysis of PGF1 --- p.61 / Chapter 4.3.4 --- Analysis of PGF2 --- p.63 / Chapter 4.3.5 --- Overall Behavior of PGF --- p.66 / Chapter 4.3.6 --- Comparisons with Other Approaches --- p.69 / Chapter 4.4 --- Time Complexity --- p.72 / Chapter 4.4.1 --- Filtering Components --- p.72 / Chapter 4.4.2 --- Abstraction Component --- p.74 / Chapter 4.4.3 --- PGF Algorithms --- p.74 / Chapter 4.5 --- Summary --- p.75 / Chapter 5 --- Integrated Concept Prototype Learner --- p.77 / Chapter 5.1 --- Motivation --- p.78 / Chapter 5.2 --- Abstraction Component --- p.80 / Chapter 5.2.1 --- Issues for Abstraction --- p.80 / Chapter 5.2.2 --- Investigation on Typicality --- p.82 / Chapter 5.2.3 --- Typicality in Abstraction --- p.85 / Chapter 5.2.4 --- The TPA algorithm --- p.86 / Chapter 5.2.5 --- Analysis of TPA --- p.90 / Chapter 5.3 --- Filtering Component --- p.93 / Chapter 5.3.1 --- Investigation on Associate --- p.96 / Chapter 5.3.2 --- The RT2 Algorithm --- p.100 / Chapter 5.3.3 --- Analysis of RT2 --- p.101 / Chapter 5.4 --- Concept Integration --- p.103 / Chapter 5.4.1 --- The ICPL Algorithm --- p.104 / Chapter 5.4.2 --- Analysis of ICPL --- p.106 / Chapter 5.5 --- Empirical Analysis --- p.106 / Chapter 5.5.1 --- Experimental Setup --- p.106 / Chapter 5.5.2 --- Results of ICPL Algorithm --- p.109 / Chapter 5.5.3 --- Comparisons with Pure Abstraction and Pure Filtering --- p.110 / Chapter 5.5.4 --- Comparisons with Other Approaches --- p.114 / Chapter 5.6 --- Time Complexity --- p.119 / Chapter 5.7 --- Summary --- p.120 / Chapter 6 --- Conclusions and Future Work --- p.122 / Chapter 6.1 --- Conclusions --- p.122 / Chapter 6.2 --- Future Work --- p.126 / Bibliography --- p.128 / Chapter A --- Detailed Information for Tested Data Sets --- p.136 / Chapter B --- Detailed Experimental Results for PGF --- p.138