Self-Supervised Representation Learning for Online Handwriting Text Classification

Self-supervised learning offers an efficient way of extracting rich representations from various types of unlabeled data while avoiding the cost of annotating large-scale datasets. This is achievable by designing a pretext task to form pseudo labels with respect to the modality and domain of the data. Given the evolving applications of online handwritten texts, in this study, we propose the novel Part of Stroke Masking (POSM) as a pretext task for pretraining models to extract informative representations from the online handwriting of individuals in English and Chinese languages, along with two suggested pipelines for fine-tuning the pretrained models. To evaluate the quality of the extracted representations, we use both intrinsic and extrinsic evaluation methods. The pretrained models are fine-tuned to achieve state-of-the-art results in tasks such as writer identification, gender classification, and handedness classification, also highlighting the superiority of utilizing the pretrained models over the models trained from scratch

Utilizing graphics processing units in cryptographic applications.

Fleissner Sebastian.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 91-95).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Legend of Hercules --- p.1Chapter 1.2 --- Background --- p.2Chapter 1.3 --- Research Purpose --- p.2Chapter 1.4 --- Research Overview --- p.3Chapter 1.5 --- Thesis Organization --- p.4Chapter 2 --- Background and Definitions --- p.6Chapter 2.1 --- General Purpose GPU Computing --- p.6Chapter 2.1.1 --- Four Generations of GPU Hardware --- p.6Chapter 2.1.2 --- GPU Architecture & Terms --- p.7Chapter 2.1.3 --- General Purpose GPU Programming --- p.9Chapter 2.1.4 --- Shader Programming Languages --- p.12Chapter 2.2 --- Cryptography Overview --- p.13Chapter 2.2.1 --- "Alice, Bob, and Friends" --- p.14Chapter 2.2.2 --- Cryptographic Hash Functions --- p.14Chapter 2.2.3 --- Secret Key Ciphers --- p.15Chapter 2.2.4 --- Public Key Encryption --- p.16Chapter 2.2.5 --- Digital Signatures --- p.17Chapter 2.3 --- The Montgomery Method --- p.18Chapter 2.3.1 --- Pre-computation Step --- p.19Chapter 2.3.2 --- Obtaining the Montgomery Representation --- p.19Chapter 2.3.3 --- Calculating the Montgomery Product(s) --- p.19Chapter 2.3.4 --- Calculating final result --- p.20Chapter 2.3.5 --- The Montgomery Exponentiation Algorithm . . --- p.20Chapter 2.4 --- Elliptic Curve Cryptography --- p.21Chapter 2.4.1 --- Introduction --- p.21Chapter 2.4.2 --- Recommended Elliptic Curves --- p.22Chapter 2.4.3 --- Coordinate Systems --- p.23Chapter 2.4.4 --- Point Doubling --- p.23Chapter 2.4.5 --- Point Addition --- p.24Chapter 2.4.6 --- Double and Add --- p.25Chapter 2.4.7 --- Elliptic Curve Encryption --- p.26Chapter 2.5 --- Related Research --- p.28Chapter 2.5.1 --- Secret Key Cryptography on GPUs --- p.28Chapter 2.5.2 --- Remotely Keyed Cryptographics --- p.29Chapter 3 --- Proposed Algorithms --- p.30Chapter 3.1 --- Introduction --- p.30Chapter 3.2 --- Chapter Organization --- p.31Chapter 3.3 --- Algorithm Design Issues --- p.31Chapter 3.3.1 --- Arithmetic Density and GPU Memory Access . --- p.31Chapter 3.3.2 --- Encoding Large Integers with Floating Point Numbers --- p.33Chapter 3.4 --- GPU Montgomery Algorithms --- p.34Chapter 3.4.1 --- Introduction --- p.34Chapter 3.4.2 --- GPU-FlexM-Prod Specification --- p.37Chapter 3.4.3 --- GPU-FlexM-Mul Specification --- p.43Chapter 3.4.4 --- GPU-FlexM-Exp Specification --- p.45Chapter 3.4.5 --- GPU-FixM-Prod Specification --- p.46Chapter 3.4.6 --- GPU-FixM-Mul Specification --- p.50Chapter 3.4.7 --- GPU-FixM-Exp Specification --- p.52Chapter 3.5 --- GPU Elliptic Curve Algorithms --- p.54Chapter 3.5.1 --- GPU-EC-Double Specification --- p.55Chapter 3.5.2 --- GPU-EC-Add Specification --- p.59Chapter 3.5.3 --- GPU-EC-DoubleAdd Specification --- p.64Chapter 4 --- Analysis of Proposed Algorithms --- p.67Chapter 4.1 --- Performance Analysis --- p.67Chapter 4.1.1 --- GPU-FlexM Algorithms --- p.69Chapter 4.1.2 --- GPU-FixM Algorithms --- p.72Chapter 4.1.3 --- GPU-EC Algorithms --- p.77Chapter 4.1.4 --- Summary --- p.82Chapter 4.2 --- Usability of Proposed Algorithms --- p.83Chapter 4.2.1 --- Signcryption --- p.84Chapter 4.2.2 --- Pure Asymmetric Encryption and Decryption --- p.85Chapter 4.2.3 --- Simultaneous Signing of Multiple Messages --- p.86Chapter 4.2.4 --- Relieving the Main Processor --- p.87Chapter 5 --- Conclusions --- p.88Chapter 5.1 --- Research Results --- p.88Chapter 5.2 --- Future Research --- p.89Bibliography --- p.9

The algebraic immersed interface and boundary method for elliptic equations with jump conditions

A new fictitious domain method, the algebraic immersed interface and boundary (AIIB) method, is presented for elliptic equations with immersed interface conditions. This method allows jump conditions on immersed interfaces to be discretized accurately. The main idea is to create auxiliary unknowns at existing grid locations which increases the degrees of freedom of the initial problem. These auxiliary unknowns allow to impose various constraints to the system on interfaces of complex shapes. For instance, the method is able to deal with immersed interfaces for elliptic equations with jump conditions on the solution or discontinuous coefficients with a second order of spatial accuracy. As the AIIB method acts on an algebraic level and only changes the problem matrix, no particular attention to the initial discretization is required. The method can be easily implemented in any structured grid code and can deal with immersed boundary problems too. Several validation problems are presented to demonstrate the interest and accuracy of the method