Dataset for Transformers to Predict the Applicability of Symbolic Integration Routines

This is the version related to the publication

Supporting code for the Tereso del Río Almajano's Thesis titled "Heuristics and Machine Learning to Improve Symbolic Computation Algorithms: Speeding Up Cylindrical Algebraic Decomposition"

This upload contains the dataset and code used throught Tereso del Río Almajano's thesis titled "Heuristics and Machine Learning to Improve Symbolic Computation Algorithms: Speeding Up Cylindrical Algebraic Decomposition" to evaluate the different strategies presented to choose the variable ordering. Moreover, this code also generates multiple figures and tables included in the thesis

A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs

This toolbox supports the results in the following publication: D. Florescu and M. England. A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs. The authors are supported by EPSRC Project EP/R019622/1: Embedding Machine Learning within Quantifer Elimination Procedures. The main script is ML_test_rand/pipeline1.py. More details can be found as comments in the script. The sotd heuristic is implemented in the file data_gen_sotd_rand_test.mw. The data is already generated in the repository. The dataset of polynomials can be found in folders entitled poly_rand_dataset (for training) and poly_rand_dataset_test (for testing). The CAD data is generated by running generate_CAD_data.py. The data is already generated in the repository. The CAD routine was run in Maple 2018, with an updated version of the RegularChains Library downloaded in February 2019 from http://www.regularchains.org. The library file is also available in this repository (RegularChains_Updated.mla) This updated library contains bug fixes and additional functionality. The training and evaluation of the machine learning models was done using the scikit-learn package v0.20.2 for Python 2.7. Some data files generated by the pipeline are included in this repository for consistency and for saving time. However, they can be generated again by the user should they wish so: - the predictions with the sotd heuristic (II(d) in the supported paper) - the ML hyperparameters, resulted from 5-fold cross-validation (I(d)i in the supported paper) - the files containing CAD runtimes (in the folders comp_times_rand_dataset and comp_times_rand_dataset_test, corresponding to I(a) and II(e) in the supported paper

SCPR_Codes

Set Covering Problem with Reasons (SCPR). The code contains randomly generated datasets for a new variant of the Set Covering Problem and Algorithms to solve it

Data and code for the paper New heuristic to choose a cylindrical algebraic decomposition variable ordering motivated by complexity analysis

This repository contains all the data and code necessary to generate the results and figures presented in the 2022 CASC paper of Tereso del Río and Matthew England. It contains a dataset in a .csv file, a .tex describing the dataset and seven .py files containing the code used to analyse the dataset. To generate the figures in the paper and the dataset used to create the table in the paper run the Python code named 'run_for_paper.py' inside the folder 'Code'

Supplementary_Files_For_SCPR

Set Covering Problem with Reasons (SCPR). The file contains randomly generated datasets, real world application data and algorithms for solving a new generalisation of the Set Covering Problem

EPSRC HEED Data Repository: Surveys

The HEED project aims at understanding energy needs of refugees and displaced populations to improve access to clean energy. The focus of HEED is on the lived experiences of refugees living for protracted periods of time in three refugee camps in Rwanda (Nyabiheke, Gihembe and Kigeme) and internally displaced persons (IDPs) forced to leave their homes as a result of the 2015 earthquake in Nepal. As part of the project, an energy assessment survey of households in both countries was undertaken using quantitative and qualitative research methods with households living in different parts of the camps/settlements, entrepreneurs running small businesses, and those responsible for community facilities, such as schools and health clinics. In the first phase, a questionnaire-based survey targeting displaced populations was conducted with households living in three refugee camps in Rwanda and four displaced sites in Nepal (see tables 2.1 and 2.2 respectively). The second phase of the field research involved a series of interviews and focus group discussions with various stakeholders in Nepal and Rwanda. The surveys were designed and delivered between March and April 2018 by the project partner, Practical Action. In both countries, the enumerators for the survey received a two-day training on research methods, data collection and ethics.  With regards to the household survey, the sample size was derived using Cochran’s formula as described by Bartlett et. al. in Organizational Research: Determining Appropriate Sample Size in Survey Research. A minimum sample size of 119 households was derived by applying a margin of error of 0.03 and an alpha of 0.5. A breakdown of the focal group and specific sites where the surveys were delivered in Rwanda and Nepal is shown in tables 2.1 and 2.2 respectively. In Rwanda, a total of 814 surveys including 622 households, 155 enterprises and 37 community facilities from across three sites were conducted. The sample distribution across camp shows 211 for Gihembe, 202 for Kigeme and 209 for Nyabiheke. In Gihembe more than half of the respondents (118, 55.9%) sampled were females with the remaining 93 (44.1%) being males. This is in contrast with Kigeme where almost equal numbers of both male (100, 49.5%) and females (102, 50.5%) were sampled. In Nyabiheke the sample covered more females (123, 58.9%) than males (86, 41.1%). In Nepal, the sample covered 181 households, 18 enterprises and 3 community facilities (see table 2.2). The household sample in Nepal covered more males (126, 69.6%) than females (55, 30.4%).  Folder Structure: Surveys: Gihembe Community Facility Survey – Gihembe_CF.csv Gihembe Enterprise Survey – Gihembe_EN.csv Gihembe Household Survey – Gihembe_HH.csv Kigeme Community Facility Survey – Kigeme_CF.csv Kigeme Enterprise Survey – Kigeme_EN.csv Kigeme Household Survey – Kigeme_HH.csv Nepal Community Facility Survey – Nepal_CF.csv Nepal Enterprise Survey – Nepal_EN.csv Nepal Household Survey – Nepal_HH.csv Nyabiheke Community Facility Survey - Nyabiheke_CF.csv Nyabiheke Enterprise Survey – Nyabiheke_EN.csv Nyabiheke Household Survey – Nyabiheke_HH.csv Location Maps: Gihembe Community Facility Survey Map – CF_GIS_gihembe.csv Gihembe Enterprise Survey Map – EN_GIS_gihembe.csv Gihembe Household Survey Map – HH_GIS_gihembe.csv Kigeme Community Facility Survey Map – CF_GIS_kigeme.csv Kigeme Enterprise Survey Map – EN_GIS_kigeme.csv Kigeme Household Survey Map – HH_GIS_kigeme.csv Nepal Community Facility Survey Map – CF_GIS_nepal.csv Nepal Enterprise Survey Map – EN_GIS_nepal.csv Nepal Household Survey Map – HH_GIS_nepal.csv Nyabiheke Community Facility Survey Map - CF_GIS_nyabiheke.csv Nyabiheke Enterprise Survey Map – EN_GIS_nyabiheke.csv Nyabiheke Household Survey Map – HH_GIS_nyabiheke.csv The following information was gathered from each of the surveys: Households: The datasets contain information about household demographics, access to and use of electricity and lighting technologies, access to and use of cooking technologies and fuels, self-reported needs and priorities by the household, and ownership of energy products. Several key areas, such as solar lighting products and issues around fuel usage, are covered in more detail. Enterprises: The datasets contain information about the enterprise, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facility: The datasets contain information about the community facility or institution, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facilities offered healthcare services were presented additional questions about specific medical devices.  The survey results together with other methodological tools including field visits, workshops - ‘Design for Displacement (D4D)’ and ‘Energy for End-Users’ (E4E) workshops have provided relevant data and contextual knowledge to inform the design of the various interventions associated with the HEED. The data sets and results have been compiled, organised and uploaded in the data portal for use by researchers, students and all both within and outside of the project consortium, during and beyond the project lifetime

Multi-Sensor based Human Activity Recognition

Dataset Description Multi-sensor data collection has been done in June,2021 at Coventry University. In this data collection, three types of sensors (Radar, InfraRed and Acoustic) were fused together by a MATLAB code. There were one sensor for radar and acoustic sensors each and three IR (Grid-Eye) sensors were integrated together to eliminate limitation of a single sensor and to get maximum benefit of Multi-Sensor Human Activity Detection. Overall, 11 subjects took part in the data-collection process which were mainly post graduate researched and academics. Collected dataset is novelty in itself as in this experiment, a series of human activities were performed rather than performing a single activity each time. The experiment was designed carefully by keeping elderly people in the mind. Each series comprise of some day-to-day activities such as walking, sitting, talking and so on. It also has some situation which need attention such as fall, asking for help and so on. In this data collection, seven set of activities were recorded among which series one to three were single subject and series four to seven were dual subject activities. Each series was performed ten times by each subject. Each series of activities were performed ten times by each subject. Description of each series is given below. Series Number of Subject Activity Series-1 Single Sit(talking)+SitTostanding(help)+walking(leftToright)(caughing)+falling(screaming) Series-2 Single Bending (Pickup Food) +walk (Right to Left) (coughing)+stand to sit(talking)+sit while eating Series-3 Single walking corner left diagonally to corner right (drop a metal spoon) +return (diagonally +help) +bending to take the spoon(talk)+standing from Bending(scream)+walking to the original corner (drop the spoon) In the folder of Data_Collection, seven folders of Series-1 to Series-7 are present. In each folder, number of series are present which were performed by each subject 10 times. Each performed series has a folder of sensor data in it. The content of sensor data and its description is given below. Sr No. File Name Type Description 1 AudioFiles (Folder) Wav File This data is collected by UMA-16 Acoustic sensor which captures sound while performing activity series 2 AcousticData MATLAB File It is in the form of 16 Channel data which has numerical values collected from Acoustic Sensor 3 GridEyeData MATLAB File Three data files data1, data2 and data3 from three GridEye Sensors were collected and it has time stamps for each frame captured in the form of t1, t2 and t3. Please refer GridEye_Read for the readable form of data1, data2 and data3 4 RadarData MATLAB Data It has range and noise data (rpVar, npVar), RangeDopplerMatrix (no. of Framesx16x256) and rangeDopplerVarArray (Frame No.x256) 5 Miscellaneous Data MATLAB File This folder has important information such as drivers name (IR Sensor), frame length, sampling frequency, folder path, current time, total experiment time and so on. Data of each sensor can be read in MATLAB and can also be visualised. It can also be converted in python data file. This pre-processing will be done before data analysis

Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition

This toolbox supports the results in the following publication: Pickering, L., del Río, T., England, M. and Cohen, K., 2023. Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition. arXiv preprint arXiv:2304.12154. Abstract: In recent years there has been increased use of machine learning (ML) techniques within mathematics, including symbolic computation where it may be applied safely to optimise or select algorithms. This paper explores whether using explainable AI (XAI) techniques on such ML models can offer new insight for symbolic computation, inspiring new implementations within computer algebra systems that do not directly call upon AI tools. We present a case study on the use of ML to select the variable ordering for cylindrical algebraic decomposition. It has already been demonstrated that ML can make the choice well, but here we show how the SHAP tool for explainability can be used to inform new heuristics of a size and complexity similar to those human-designed heuristics currently commonly used in symbolic computation

Survey Data from the SET Project

Survey Data from the SET project. The questions and results are provided for initial surveys conducted in Nov 2023, monthly check-in surveys and an exit survey in Aug 2024. Four check-in surveys were carried out before the e-cooker was installed along with four subsequent check-in surveys conducted after installation. To anonymise the data, some cells have either been removed or marked with an 'x'. The household kitchen sensor data can be found in version 1: 10.5281/zenodo.13192469 The e-Cooker PV-battery system and metered appliance data can be found in version 2: 10.5281/zenodo.13896192 We would like to acknowledge the financial support of the Innovate UK, Energy Catalyst programme for funding the Solar Energy Transitions (SET) Inclusive e-cooking in sub-Saharan Africa Project (10044025). We would like to thank project delivery partners Coventry University, Mesh Power and Rwanda Energy Group and recognise Climate Solutions Consulting for their significant technical input. Our thanks extends to Kayonza District and its district leaders, REG’s community mobilisers and translators, Mesh Power’s technical support team and the Coventry University Africa Hub. Our profound thanks goes to all the participating households, whose engagement was crucial to the project’s success. To cite, please use: Nixon, J., Halford, A., Ashraf, R., Habyarimana, C., Kabananiye, J., Lefebvre, O., & Mori, R. (2024). Data from the SET Project [Data set]. Zenodo. 10.5281/zenodo.13192468