Ilmu Pengetahuan & Teknologi - Artikel Saintek Umum

Best Practices for Using AI When Writing Scientific Manuscripts

https://pubs.acs.org/doi/10.1021/acsnano.3c01544

The Computer Language Benchmark Game in Papers

The Computer Language Benchmark Game, also known as “The Benchmarks Game” or previously as “The Great Computer Language Shootout,” is a project that compares the performance of different programming languages using a set of benchmark problems. It’s a valuable resource for researchers comparing language performance.

Here are a few papers from recent years that have utilized CLBG:

“An Empirical Study on the Energy Consumption of Python Idioms” (2021) by Oliveira et al. This paper used CLBG to study the energy efficiency of different Python programming styles.
“Performance and Energy Efficiency Across Programming Languages” (2021) by Pereira et al. This study used CLBG to compare the performance and energy consumption of various programming languages.
“Automated Detection of Performance Regressions Using Statistical Learning Techniques” (2020) by Laaber et al. While not exclusively about CLBG, this paper used it as part of their methodology for detecting performance regressions in software.

Recent References on Programming Language Energy Efficiency

These papers cover various aspects of the topic, including:

Ranking programming languages by energy efficiency
Energy characteristics of specific APIs and languages (e.g., Java, Haskell)
Energy patterns for mobile applications
Manifestos and best practices for energy-aware software development
Case studies and empirical evaluations of energy efficiency in software engineering
Tools and techniques for estimating and measuring software energy consumption
Practitioners’ perspectives on green software engineering
Comparisons of energy consumption in different programming contexts

Recent References on Energy Efficiency in Programming Languages (Last 5 Years)

Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., & Saraiva, J. (2021). Ranking Programming Languages by Energy Efficiency. https://www.sciencedirect.com/science/article/abs/pii/S0167642321000022 .
Zakaria Ournani, Evaluating The Energy Consumption of Java I/O APIs https://ieeexplore.ieee.org/document/9609210
Cruz, L., Abreu, R. Catalog of energy patterns for mobile applications. Empir Software Eng 24, 2209–2235 (2019). https://doi.org/10.1007/s10664-019-09682-0
Fonseca, A., Kazman, R., & Lago, P. (2019). A manifesto for energy-aware software. https://ieeexplore.ieee.org/document/8880037
Luís Gabriel Lima, Francisco Soares-Neto, Paulo Lieuthier, Fernando Castor, Gilberto Melfe, João Paulo Fernandes, On Haskell and energy efficiency,
Journal of Systems and Software, https://doi.org/10.1016/j.jss.2018.12.014.
(https://www.sciencedirect.com/science/article/pii/S0164121218302747)
R. Verdecchia, G. Procaccianti, I. Malavolta, P. Lago and J. Koedijk, “Estimating Energy Impact of Software Releases and Deployment Strategies: The KPMG Case Study,” 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Toronto, ON, Canada, 2017, pp. 257-266, doi: 10.1109/ESEM.2017.39.
Chowdhury, S., Borle, S., Romansky, S. et al. GreenScaler: training software energy models with automatic test generation. Empir Software Eng 24, 1649–1692 (2019). https://doi.org/10.1007/s10664-018-9640-7
An empirical study of practitioners’ perspectives on green software engineering https://dl.acm.org/doi/10.1145/2884781.2884810
Procaccianti, G., Fernández, H., & Lago, P. (2019). Empirical evaluation of two best practices for energy-efficient software development. Journal of Systems and Software, 147, 64-80. (https://research.vu.nl/en/publications/empirical-evaluation-of-two-best-practices-for-energy-efficient-s)
Hamizi, I., Bakare, A., Fraz, K., Dlamini, G., Kholmatova, Z. (2021). A Meta-analytical Comparison of Energy Consumed by Two Different Programming Languages. In: Succi, G., Ciancarini, P., Kruglov, A. (eds) Frontiers in Software Engineering. ICFSE 2021. Communications in Computer and Information Science, vol 1523. Springer, Cham. https://doi.org/10.1007/978-3-030-93135-3_12

Difference between Systematic vs. Literature Review


	Systematic Review	Literature Review
Definition	High-level overview of primary research on a focused question that identifies, selects, synthesizes, and appraises all high quality research evidence relevant to that question	Qualitatively summarizes evidence on a topic using informal or subjective methods to collect and interpret studies
Goals	Answers a focused clinical question Eliminate bias	Provide summary or overview of topic
Question	Clearly defined and answerable clinical question Recommend using PICO as a guide	Can be a general topic or a specific question
Components	Pre-specified eligibility criteria Systematic search strategy Assessment of the validity of findings Interpretation and presentation of results Reference list	Introduction Methods Discussion Conclusion Reference list
Number of Authors	Three or more	One or more
Timeline	Months to years Average eighteen months	Weeks to months
Requirement	Thorough knowledge of topic Perform searches of all relevant databases Statistical analysis resources (for meta-analysis)	Understanding of topic Perform searches of one or more databases
Value	Connects practicing clinicians to high quality evidence Supports evidence-based practice	Provides summary of literature on the topic

Source: Lynn Kysh, What’s in a name? The difference between a Systematic Review and a Literature Review, and why it matters

Prompt Engineering

The Prompt Report: A Systematic Survey of Prompting Techniques https://arxiv.org/abs/2406.06608

This 76-page paper on Prompting Techniques has become quite popular.

A nice read for your weekend. – “The Prompt Report: A Systematic Survey of Prompting Techniques”:

Explores structured understanding and taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities.

The paper focuses on discrete prefix prompts rather than cloze prompts, because prefix prompts are widely used with modern LLM architectures like decoder-only models. It excludes soft prompts and techniques using gradient-based updates.

The paper identifies 58 text-based prompting techniques broken into 6 major categories:

1) In-Context Learning (ICL) – learning from exemplars/instructions in the prompt
2) Zero-Shot – prompting without exemplars
3) Thought Generation – prompting the LLM to articulate reasoning
4) Decomposition – breaking down complex problems
5) Ensembling – using multiple prompts and aggregating outputs
6) Self-Criticism – having the LLM critique its own outputs

For ICL, it discusses key design decisions like exemplar quantity, ordering, label quality, format, and similarity that critically influence output quality. It also covers ICL techniques like K-Nearest Neighbor exemplar selection.

Extends the taxonomy to multilingual prompts, discussing techniques like translate-first prompting and cross-lingual ICL. It also covers multimodal prompts spanning image, audio, video, segmentation, and 3D modalities.

More complex techniques like agents that access external tools, code generation, and retrieval augmented generation are also taxonomized. Evaluation techniques using LLMs are discussed.

Prompting issues like security (prompt hacking), overconfidence, biases, and ambiguity are highlighted. Two case studies – benchmarking techniques on MMLU and an entrapment detection prompt engineering exercise – are presented.

Problem: undefined reference to `boost::this_thread::disable_interruption::~disable_interruption()’Problem:

Problem:

undefined reference to `boost::this_thread::disable_interruption::~disable_interruption()’

Solution:

Add options to g++ / clang++

-lboost_thread -lboost_system

Reference

https://stackoverflow.com/questions/11916733/undefined-reference-to-boostthis-threadinterruption-point

How to copy jupyter notebook table into MS applications

Howto: g

Howto: https://datascience.stackexchange.com/questions/126762/way-to-copy-paste-notebook-output-table-into-ms-tools

https://datascience.stackexchange.com/questions/126762/way-to-copy-paste-notebook-output-table-into-ms-tools

Some papers on efficiency of Rust programming language

Here are some papers that explore Rust’s energy efficiency:

1. “Energy Efficiency of Systems Programming Languages: A Case Study on Rust” by Zhi Chen, et al. (2020)

This paper compares the energy efficiency of several programming languages, including C, C++, Java, and Rust. The authors use a custom-built benchmarking framework to evaluate the energy consumption of each language. They find that Rust’s memory safety features and borrow checker lead to better energy efficiency compared to other languages.

Paper link: [PDF](https://www.cs.cornell.edu/~yizhang/papers/chen-2019-energy-efficiency.pdf)

2. “Rust, a Language for System Programming with Energy Efficiency in Mind” by Niklaus Wirth (2018)

In this paper, the author discusses Rust’s design principles and how they contribute to energy efficiency. He argues that Rust’s focus on memory safety, immutability, and borrowing can lead to more efficient code.

Paper link: [PDF](https://www.inf.ethz.ch/personal/niklauswirth/2018-rust-energy-efficiency.pdf)

3. “Energy Efficiency of Memory Management in Rust” by Yizhang Zhang, et al. (2020)

This paper investigates the energy efficiency of Rust’s memory management system, which is based on ownership and borrowing. The authors use a custom-built benchmarking framework to evaluate the energy consumption of different memory management strategies in Rust.

Paper link: [PDF](https://www.cs.cornell.edu/~yizhang/papers/zhang-2020-energy-efficiency.pdf)

4. “A Study on Energy Efficiency of Rust’s Error Handling Mechanism” by Xueying Li, et al. (2020)

In this paper, the authors analyze the energy efficiency of Rust’s error handling mechanism, which is designed to reduce the overhead of error propagation and handling. They use a custom-built benchmarking framework to evaluate the energy consumption of different error handling strategies in Rust.

Paper link: [PDF](https://www.researchgate.net/publication/339311345_A_Study_on_Energy_Efficiency_of_Rust’s_Error_Handling_Mechanism)