Demo Data from VLDB 2026 / reviewers in the wild / expert
John Waldron
dblp:04/4639
· DBLP profile ↗
11ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 4Human-computer interaction and ubiquitous computing · 3Systems, architecture and hardware · 2Security and privacy · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
GPUs and heterogeneous computing · 100% | |
| Network and information security
2 papers |
Cryptographic primitives and cryptanalysis · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis
symmetric cryptography |
0.2 | 2 | 2008 | Practical Symmetric Key Cryptography on Modern Graphics Hardware · USENIX Security Symposium 2008 AES Encryption Implementation and Analysis on Commodity Graphics Processing Units · CHES 2007 |
GPUs and heterogeneous computing › GPU computing
cryptographic acceleration |
0.1 | 1 | 2008 | Practical Symmetric Key Cryptography on Modern Graphics Hardware · USENIX Security Symposium 2008 |
GPUs and heterogeneous computing
graphics hardware |
0.1 | 1 | 2008 | Practical Symmetric Key Cryptography on Modern Graphics Hardware · USENIX Security Symposium 2008 |
Cryptographic primitives and cryptanalysis › block cipher
AES |
0.1 | 1 | 2007 | AES Encryption Implementation and Analysis on Commodity Graphics Processing Units · CHES 2007 |
GPUs and heterogeneous computing › GPU computing › GPGPU acceleration
GPU-accelerated cryptography |
0.1 | 1 | 2007 | AES Encryption Implementation and Analysis on Commodity Graphics Processing Units · CHES 2007 |
GPUs and heterogeneous computing
GPU computing |
0.1 | 1 | 2007 | AES Encryption Implementation and Analysis on Commodity Graphics Processing Units · CHES 2007 |
Methods — techniques the papers use, named apart from their topics
GPU acceleration · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Calibration and Analysis of Source Code Similarity Measures for Verilog Hardware Description Language ProjectsabstractIn this paper we report on our experiences during a first-year course on digital logic design using the Verilog hardware description language. As part of the course the students were given a series of take-home assignments, which were then marked using an automated assessment system developed by the authors. During the course the instructor was made aware that a set of solutions had been circulated to the students, and was asked to assess the impact that this had on the assessment regime. In order to answer this question, we examined and implemented a number of approaches to calculating similarity between Verilog programs, and we present the results of that study in this paper. An important feature of this work was ensuring that the measurements used were well-understood, properly calibrated and defensible. We report on the results of this study, applied to a class of 115 students who completed up to 11 projects each. James F. Power, John Waldron |
SIGCSE | 2 |
| 2019 | Quantifying Activity and Collaboration Levels in Programming AssignmentsabstractThis paper presents an experience report from a third-year undergraduate compiler design course that is taught as part of a four year computer science degree. We analyse data from a study of practical assignments, evaluated in the context of take-home formative assignments and a supervised summative examination. We implement metrics to quantify the degrees of similarity between submissions for programming assignments, as well as measuring the level of activity. We present the results of our study, and discuss the utility of these metrics for our teaching practice. James F. Power, John Waldron |
ITiCSE | 2 |
| 2009 | PPPJ special issue-Foreword
Markus Aleksy, Vasco Amaral 0001, Ralf Gitzel, James F. Power, John Waldron |
Sci. Comput. Program. | 5 |
| 2008 | Practical Symmetric Key Cryptography on Modern Graphics Hardware
Owen Harrison, John Waldron |
USENIX Security Symposium | 2 |
| 2008 | PPPJ 2006 special issue - foreword
Markus Aleksy, Ralf Gitzel, John Waldron, James F. Power |
Sci. Comput. Program. | 3 |
| 2007 | AES Encryption Implementation and Analysis on Commodity Graphics Processing Units
Owen Harrison, John Waldron |
CHES | 2 |
| 2006 | Foreword
Markus Aleksy, Ralf Gitzel, John Waldron |
Sci. Comput. Program. | 3 |
| 2005 | A method-level comparison of the Java Grande and SPEC JVM98 benchmark suitesabstractAbstract In this paper we seek to provide a foundation for the study of the level of use of object‐oriented techniques in Java programs in general, and scientific applications in particular. Specifically, we investigate the profiles of Java programs from a number of perspectives, including the use of class library methods, the size of methods called, the mode of invoke instruction used and the polymorphicity of call sites. We also present a categorization of the nature of small methods used in Java programs. We compare the Java Grande and SPEC JVM98 benchmark suites, and note a significant difference in the nature and composition of these suites, with the programs from the Java Grande suite demonstrating a less object‐oriented approach. Copyright © 2005 John Wiley & Sons, Ltd. David Gregg, James F. Power, John Waldron |
Concurr. Pract. Exp. | 3 |
| 2005 | Editorial
James F. Power, John Waldron |
Sci. Comput. Program. | 2 |
| 2004 | Assessing the assessment of programming ability
Charlie Daly, John Waldron |
SIGCSE | 2 |
| 2003 | Platform independent dynamic Java virtual machine analysis: the Java Grande Forum benchmark suiteabstractAbstract In this paper we present a platform independent analysis of the dynamic profiles of Java programs when executing on the Java Virtual Machine. The Java programs selected are taken from the Java Grande Forum benchmark suite and five different Java‐to‐bytecode compilers are analysed. The results presented describe the dynamic instruction usage frequencies, as well as the sizes of the local variable, parameter and operand stacks during execution on the JVM. These results, presenting a picture of the actual (rather than presumed) behaviour of the JVM, have implications both for the coverage aspects of the Java Grande benchmark suites, for the performance of the Java‐to‐bytecode compilers and for the design of the JVM. Copyright © 2003 John Wiley & Sons, Ltd. David Gregg, James F. Power, John Waldron |
Concurr. Comput. Pract. Exp. | 3 |