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1. On Vulnerability and Security Log analysis: A Systematic Literature Review on Recent Trends

   https://doi.org/10.1145/3400286.3418261  

   Svacina, Jan; Raffety, Jackson; Woodahl, Connor; Stone, Brooklynn; Cerny, Tomas; Bures, Miroslav; Shin, Dongwan; Frajtak, Karel; Tisnovsky, Pavel (October 2020, RACS '20: Proceedings of the International Conference on Research in Adaptive and Convergent Systems)

   null (Ed.)

   Log analysis is a technique of deriving knowledge from log files containing records of events in a computer system. A common application of log analysis is to derive critical information about a system's security issues and intrusions, which subsequently leads to being able to identify and potentially stop intruders attacking the system. However, many systems produce a high volume of log data with high frequency, posing serious challenges in analysis. This paper contributes with a systematic literature review and discusses current trends, advancements, and future directions in log security analysis within the past decade. We summarized current research strategies with respect to technology approaches from 34 current publications. We identified limitations that poses challenges to future research and opened discussion on issues towards logging mechanism in the software systems. Findings of this study are relevant for software systems as well as software parts of the Internet of Things (IoT) systems. 

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2. Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

   https://doi.org/10.1145/3568020  

   Bhuiyan, Farzana Ahamed; Rahman, Akond (May 2023, ACM Transactions on Privacy and Security)

   Adversarial attacks against supervised learning a algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects. We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to six attacks related to supervised learning algorithms. Using Lo g Assistant to conduct P ostmortems for Su pervised L earning ( LOPSUL ) , we quantify the frequency of the identified log-related coding patterns with 278 open-source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category. 

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3. Quantum many-body physics calculations with large language models

   https://doi.org/10.1038/s42005-025-01956-y  

   Pan, Haining; Mudur, Nayantara; Taranto, William; Tikhanovskaya, Maria; Venugopalan, Subhashini; Bahri, Yasaman; Brenner, Michael P; Kim, Eun-Ah (January 2025, Communications Physics)

   Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly-used approximation method in quantum physics: the Hartree-Fock method, requiring an analytic multi-step calculation deriving approximate Hamiltonian and corresponding self-consistency equations. To carry out the calculations using LLMs, we design multi-step prompt templates that break down the analytic calculation into standardized steps with placeholders for problem-specific information. We evaluate GPT-4’s performance in executing the calculation for 15 papers from the past decade, demonstrating that, with the correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases. Aggregating across all research papers, we find an average score of 87.5 (out of 100) on the execution of individual calculation steps. We further use LLMs to mitigate the two primary bottlenecks in this evaluation process: (i) extracting information from papers to fill in templates and (ii) automatic scoring of the calculation steps, demonstrating good results in both cases. 

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4. Simulation Files for Organic Contaminants and Atmospheric Nitrogen at the Graphene–Water Interface

   https://doi.org/10.5281/zenodo.6050816  

   Thakkar, Ravindra; Sandun, Gajaweera; Comer, Jeffrey (January 2022, Zenodo)

   {"Abstract":["This data set provides files needed to run the simulations described in the manuscript entitled "Organic contaminants and atmospheric nitrogen at the graphene\u2013water interface: A simulation study" using the molecular dynamics software NAMD and LAMMPS. The output of the simulations, as well as scripts used to analyze this output, are also included. The files are organized into directories corresponding to the figures of the main text and supplementary information. They include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD or LAMMPS configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and some trajectories in dcd format (downsampled). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts. A modified LAMMPS C++ source file is also included.<\/p>"],"Other":["This material is based upon work supported by the National Science Foundation under Grant No. DMR-1945589. A majority of the computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CHE-1726332, CNS-1006860, EPS-1006860, and EPS-0919443. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) and Stampede2 at the Texas Advanced Computing Center through allocation BIO200030, which is supported by National Science Foundation grant number ACI-1548562."]} 

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5. Multi-source Log Clustering in Distributed Systems

   https://doi.org/10.1007/978-981-33-6385-4_4  

   Raffety, J.; Stone, B.; Svacina, J.; Woodahl, C.; Cerny, T.; Tisnovsky, P. (April 2021, Information Science and Applications. Lecture Notes in Electrical Engineering)

   null; null; null (Ed.)

   Distributed systems are seeing wider use as software becomes more complex and cloud systems increase in popularity. Preforming anomaly detection and other log analysis procedures on distributed systems have not seen much research. To this end, we propose a simple and generic method of clustering log statements from separate log files to perform future log analysis. We identify variable components of log statements and find matches of these variables between the sources. After scoring the variables, we select the one with the highest score to be the clustering basis. We performed a case study of our method on the two open-source projects, to which we found success in the results of our method and created an open-source project log-matcher. 

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