European GBIF nodes meeting 2013: Intelligent technology for automating digitisation
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This document describes an intelligent technology system for automating the digitization of biological specimens. The system uses a conveyor belt, digital cameras, and PCs connected via a network to maximize digitization throughput. It takes high resolution photos of samples and can be customized to different digitization modes for things like plants or insects to take targeted photos. Since starting production in February 2013, the system has been digitizing 120 samples per hour.
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Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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- 1. Intelligent technology for automating digitisation Tero Mononen, European GBIF Nodes Meeting 2013, 6.3.2013, Joensuu
- 2. Technology ● Basis ○ Maximizing digitisation throughput by automating all possible ○ Cost-effective ○ Adaptive, modular ○ COTS components ● Main components ○ Conveyor belt system ○ Digital cameras ○ PCs ● Digitisation environment ○ Servers ○ Network communication
- 3. Digitisation tiers Operator tier Data tier Server backend Automation tier Photography camera pool Digitisation controller Transportation controller Photography camera controller DBs Storage FS WEB Services User interface TCP/IP networking Live camera pool
- 5. Machine vision ● Based on 1280 x 800 px real-time video stream ● Above the conveyor belt ● 10 FPS
- 6. Machine vision Plant example
- 7. Machine vision Insect example
- 8. Image analysis 6048 x 4032 px
- 9. Modes Building sample specific functionalities ● A Mode is a predefined series of actions in digitisation for specific sample type ● Currently implemented modes ○ Plant mode ■ Two photos per sample (top and bottom) ■ 1 cm overlapping area ○ Insect mode ■ Search of insects and focusing points ■ Two photos per insect (insect and labels)
- 10. Results ● Production started on 18.2.2013 ● 120 samples per hour ● About 10,000 plant samples digitised
- 11. to be continued in demonstration... Thank you!