Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools-Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures. We present a GPU-based approach which enables interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we present a simple and efficient mesh simplification algorithm towards GPU architecture. Second, we propose a novel GPU out-of-core approach that adopts a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel algorithm of mesh simplification and the GPU out-of-core approach significantly improve the overall rendering performance.

The design, development, and evaluation of a serious game that gamifies the Apollo lunar exploration missions are presented. The gamification methodology and underlying implementation are motivated by the goal of game-based learning for youth and young adults and the intention to promote their interest in STEM learning. The game is a multidisciplinary composition that simulates a set of lunar roving activities including planning a traversing route, loading communication and science equipment on the lunar rover, and driving the rover to explore the lunar surface. Accurate 3D terrains and spacecraft models are created and used in the game to maintain the high realism of the environment. Every gameplay step reflects real scientific procedures and behaviors that the astronauts performed in the past on the Moon. A usability study is conducted to gather feedback from a group of participants of different ages and with different game experience. The results of the study show the success of fostering great user engagement in STEM learning.

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Massive parallelism, and energy efficiency of GPUs, along with advances in their programmability with OpenCL and CUDA programming models have made them attractive for general-purpose computations across many application domains. Techniques for testing GPU kernels have emerged recently to aid the construction of correct GPU software. However, there exists no means of measuring quality and effectiveness of tests developed for GPU kernels. Traditional coverage criteria over CPU programs is not adequate over GPU kernels as it uses a completely different programming model and the faults encountered may be specific to the GPU architecture.We address this need in this paper and present a framework, CLTestCheck, for assessing quality of test suites developed for OpenCL kernels. The framework has the following capabilities, 1. Measures kernel code coverage using three different coverage metrics that are inspired by faults found in real kernel code, 2. Seeds different types of faults in kernel code and measures fault finding capability of test suite, 3. Simulates different work-group schedules to check for potential deadlocks and data races with a given test suite. We conducted empirical evaluation of CLTestCheck on a collection of 82 publicly available GPU kernels and test suites. We found that CLTestCheck is capable of automatically measuring effectiveness of test suites, in terms of kernel code coverage, fault finding and revealing data races in real OpenCL kernels.