Rob Lewis - Presentation at Emerging Communications Conference & Awards (eComm 2011)

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Unified Communications – 2011 ,[object Object],Audio - Voice Only Text - SMS/Chat Presence Shared desktop / IWB Communication modality switching

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Rob Lewis - Presentation at Emerging Communications Conference & Awards (eComm 2011)

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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Rob Lewis - Presentation at Emerging Communications Conference & Awards (eComm 2011)

4. Unified Communications – Emerging Unified Communicators Call, Text, Chat, Video Real time text translation Cross modality communication Voice Language Translation Docomo in conversation Google real time voice translation Russian ATM Lie detection

5. Person to Person translation Intent Learning Preferences Behavioral Heuristics CWIM Communicate What I Mean Perceive What They Meant Intentionally / accidentally Unified Communications – 2025

6. Male to Female Communications Report vs. Rapport (Relationship) Status vs. Support Independence vs. Intimacy Advice vs. Understanding Orders vs. Proposals Conflict vs. Compromise Source: You Just Don’t Understand, 1990, Deborah Tannen PhD

7. NLP Rapport Visual “Do you see my point” “It seems clear” Body Language Audio “Listen to what I’m saying” Kinesthetic “I felt as if…” “I feel like we really connected” * Instant Rapport, Michael Brooks

8. 2025 Mirrored Communications

9. Unified Communications 2025

10. 2025 Translation Language Modality Intent Integrity Gender Rapport

11. Example 2025 Rapport: Kinesthetic – Visual Intent: Honest Rapport: Visual - Kinesthetic Intent: Sarcastic

Rob Lewis - Presentation at Emerging Communications Conference & Awards (eComm 2011)

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