---[About Paper]:

First, this 1-page paper was delivered as part of a keynote talk at the 3rd International SLE conference that took place in Eindhoven, Netherlands in 2010[3]. It touches on his work concerning the Choice Calculus (CC) meant to formalize as well as streamline future work relating to variability in software artefacts, systems or expressions[4]. It relates to popular work in the Programming Language Engineering field by its relation to the Lambda Calculus[4], and though not presented in this abstract paper, the CC's language syntax, semantics and potential applications are introduced; Choices & Dimensions that group Choices.


---[REFS]:

1. https://engineering.oregonstate.edu/people/martin-erwig

2. https://www.amazon.in/stores/author/B004575Y1O/about

3. https://www.sleconf.org/2010/

4. Erwig, M. (2010). A language for software variation research. ACM SIGPLAN Notices, 46(2), 3-12. URL: https://web.engr.oregonstate.edu/~erwig/papers/VariationLang_GPCE10.pdf

Hello students, researchers, faculty and visitors! Perhaps we'll take a break from usual Blackboard Adventures around Christmas---for good measure, nonetheless, we'll likely want to quickly wrap up some unfinished R&D business before 2024 closes. So, wish me well 🤞😃🤭

Reviewing ACM SLE papers continues... 👇🏻👇🏻

With a somewhat long list of co-authors, the "Trellis paper" [3] (not to be confused with "Tetris" the game) is among the most exciting SLE papers I've come across thus far. Published this year and surprisingly spearheaded by a PhD student; Lars Hummelgren (and not some professor such as many past ACM SLE papers we've covered here on BA), this is such a terrific work touching on several important matters in contemporary Machine Learning research. Interestingly, Lars's second name almost feels like the HMMs he's researching here!

---[About Paper]:

The paper[3] is about a popular machine/statistical learning technique; HMMs; Hidden Markov Models, specifically about the sparse kind. It has a great mini intro to HMMs for newcomers too. As with most SLE papers, focus is on a new software language; Trellis in this case, which provably and empirically has proven to be more performant in time-series learning problems involving sparse datasets (arguably more representative of realistic problems). Trellis is a nonexecutable DSL[4] that takes a Trellis model of a sparse HMM and compiles it into a Python library with which CUDA-powered learning can be executed via a clean API[3]. Paper delves into the details of that process, as well as discussing interesting related previous work too.


---[REFS]:

1. https://www.kth.se/profile/larshum

2. https://scholar.google.com/citations?user=l6ppBDIAAAAJ&hl=en

3. Hummelgren, L., Palmkvist, V., Stjerna, L., Xu, X., Jaldén, J., & Broman, D. (2024, October). Trellis: A Domain-Specific Language for Hidden Markov Models with Sparse Transitions. In Proceedings of the 17th ACM SIGPLAN International Conference on Software Language Engineering (pp. 196-209). URL: https://dl.acm.org/doi/pdf/10.1145/3687997.3695641

4. Lutalo, Joseph Willrich. "Software Language Engineering-Text Processing Language Design, Implementation, Evaluation Methods." (2024). URL: https://www.preprints.org/frontend/manuscript/3903e4cd075074a7005cb705a5ef26c5/download_pub


#review #notes #acm #sle #jwl #phd

End of Year Greetings from our Internet President

We explore a novel approach to enhancing voice-operated personal assistants by integrating a lightweight text processing language, TEA, into our existing Question-Answer Knowledge Bases (QAKBs). This allows for dynamic, context-aware responses and multi-turn interactions, paving the way for smarter and more adaptable AI assistants. I invite you to read the abstract paper and share your thoughts! Also, looking forward to getting some support or a research grant to help further work we started years ago finally bringing together the VOSA and TEA language projects!

And thus we've adventured towards the new year of 2025.. through thick and thin, across terrain and hurdles of many kinds. Greetings to you that's been with us along this journey. Let's hope for and look forward to an even more productive, more impactful year ahead.