More Like this

1. Dozer: Migrating Shell Commands to Ansible Modules via Execution Profiling and Synthesis

   Horton, Eric ; Parnin, Chris ( January 2022 , Proceedings of the International Conference on Software Engineering)

   Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this problem with Dozer, a technique to help developers push their shell commands into Ansible task definitions. It operates by tracing and comparing system calls to find Ansible modules with similar behaviors to shell commands, then generating and validating migrations to find the task which produces the most similar changes to the system. Dozer is syntax agnostic, which should allow it to generalize to other configuration management platforms. We evaluate Dozer using datasets from open source configuration scripts. 

   more » « less
2. Templates for writing PyMOL scripts

   https://doi.org/10.1002/pro.3997  

   Mooers, Blaine H. M. ; Brown, Marina E. ( November 2020 , Protein Science)

   PyMOLcommands are used to exert exquisite control over the appearance of a molecular model. This control has madePyMOLpopular for making images of protein structures for publications and presentations. However, many users have poor recall of the commands due to infrequent use ofPyMOL. This poor recall hinders the writing of new code in scripts. One solution is to build the new script by using code fragments as templates for modular parts of the task at hand. The code fragments can be accessed from a library while writing the code from inside a text editor (e.g., Visual Studio Code, Vim, and Emacs). We developed a library ofPyMOLcode templates or snippets calledpymolsnipsto ease the writing ofPyMOLcode in scripts. We madepymolsnipsavailable on GitHub in formats for 18 popular text editors. Most of the supported text editors are available for Mac, Windows, and Linux operating systems. The GitHub site includes animations that complement the instructions for installing the library for each text editor. We expect that the library will help manyPyMOLusers to be more productive when writingPyMOLscript files.

    

   more » « less
3. SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models

   https://doi.org/10.1609/aiide.v19i1.27504  

   Kumaran, Vikram ; Rowe, Jonathan ; Mott, Bradford ; Lester, James ( October 2023 , Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.

    

   more » « less
4. Experimentation Framework for Wireless Communication Systems under Jamming Scenarios Dataset

   https://doi.org/10.5281/zenodo.4749668  

   Jacovic, Marko ( January 2021 , Zenodo)

   Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
   Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. 

   Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

   ---------------------------------------------------------------------------------------------

   Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

   --------------------------------

   logs:    - data logs collected from devices under test
       - 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. 
       - 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state. 
       - 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer. 

   --------------------------------

   parsers:    - scripts used to collect or process the log files used in the study
           - 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary). 
           - 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated. 
           - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. 

   --------------------------------

   rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
               - 'channelSummary.pdf': summary of channel statistics from ray-tracing study
               - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file 
               - 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired
               - DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)
               - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required
               - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')

   --------------------------------

   results:    - contains performance results used in paper based on parsing of aforementioned log files
    

    

   more » « less
5. SDTA: An Algebra for Statistical Data Transformation

   https://doi.org/10.1145/3468791.3468811  

   Song, Jie ; Jagadish, H. V. ; Alter, George ( July 2021 , 33rd International Conference on Scientific and Statistical Database Management)

   null (Ed.)

   Statistical data manipulation is a crucial component of many data science analytic pipelines, particularly as part of data ingestion. This task is generally accomplished by writing transformation scripts in languages such as SPSS, Stata, SAS, R, Python (Pandas) and etc. The disparate data models, language representations and transformation operations supported by these tools make it hard for end users to understand and document the transformations performed, and for developers to port transformation code across languages. Tackling these challenges, we present a formal paradigm for statistical data transformation. It consists of a data model, called Structured Data Transformation Data Model (SDTDM), inspired by the data models of multiple statistical transformations frameworks; an algebra, Structural Data Transformation Algebra (SDTA), with the ability to transform not only data within SDTDM but also metadata at multiple structural levels; and an equivalent descriptive counterpart, called Structured Data Transformation Language (SDTL), recently adopted by the DDI Alliance that maintains international standards for metadata as part of its suite of products. Experiments with real statistical transformations on socio-economic data show that SDTL can successfully represent 86.1% and 91.6% respectively of 4,185 commands in SAS and 9,087 commands in SPSS obtained from a repository. We illustrate with examples how SDTA/SDTL could assist with the documentation of statistical data transformation, an important aspect often neglected in metadata of datasets.We propose a system called C2Metadata that automatically captures the transformation and provenance information in SDTL as a part of the metadata. Moreover, given the conversion mechanism from a source statistical language to SDTA/SDTL, we show how functional-equivalent transformation programs could be converted to other functionally equivalent programs, in the same or different language, permitting code reuse and result reproducibility, We also illustrate the possibility of using of SDTA to optimize SDTL transformations using rule-based rewrites similar to SQL optimizations. 

   more » « less