GSM8K - Grade School Math 8K Q&A

A Linguistically Diverse Dataset for Multi-Step Reasoning Question Answering

By Huggingface Hub [source]

About this dataset

This Grade School Math 8K Linguistically Diverse Training & Test Set is designed to help you develop and improve your understanding of multi-step reasoning question answering. The dataset contains three separate data files: the socratic_test.csv, main_test.csv, and main_train.csv, each containing a set of questions and answers related to grade school math that consists of multiple steps. Each file contains the same columns: question, answer. The questions contained in this dataset are thoughtfully crafted to lead you through the reasoning journey for arriving at the correct answer each time, allowing you immense opportunities for learning through practice. With over 8 thousand entries for both training and testing purposes in this GSM8K dataset, it takes advanced multi-step reasoning skills to ace these questions! Deepen your knowledge today and master any challenge with ease using this amazing GSM8K set!

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How to use the dataset

This dataset provides a unique opportunity to study multi-step reasoning for question answering. The GSM8K Linguistically Diverse Training & Test Set consists of 8,000 questions and answers that have been created to simulate real-world scenarios in grade school mathematics. Each question is paired with one answer based on a comprehensive test set. The questions cover topics such as algebra, arithmetic, probability and more.

The dataset consists of two files: main_train.csv and main_test.csv; the former contains questions and answers specifically related to grade school math while the latter includes multi-step reasoning tests for each category of the Ontario Math Curriculum (OMC). In addition, it has three columns - Question (Question), Answer ([Answer]) – meaning that each row contains 3 sequential question/answer pairs making it possible to take a single path from the start of any given answer or branch out from there according to the logic construction required by each respective problem scenario; these columns can be used in combination with text analysis algorithms like ELMo or BERT to explore different formats of representation for responding accurately during natural language processing tasks such as Q&A or building predictive models for numerical data applications like measuring classifying resource efficiency initiatives or forecasting sales volumes in retail platforms..

To use this dataset efficiently you should first get familiar with its structure by reading through its documentation so you are aware all available info regarding items content definition & format requirements then study examples that best suits your specific purpose whether is performing an experiment inspired by education research needs, generate insights related marketing analytics reports making predictions over artificial intelligence project capacity improvements optimization gains etcetera having full access knowledge about available source keeps you up & running from preliminary background work toward knowledge mining endeavor completion success Support User success qualitative exploration sessions make sure learn all variables definitions employed heterogeneous tools before continue Research journey starts experienced Researchers come prepared valuable resource items employed go beyond discovery false alarm halt advancement flow focus unprocessed raw values instead ensure clear cutting vision behind objectives support UserHelp plans going mean project meaningful campaign deliverables production planning safety milestones dovetail short deliveries enable design interfaces session workforce making everything automated fun entry functioning final transformation awaited offshoot Goals outcome parameters monitor life cycle management ensures ongoing projects feedbacks monitored video enactment resources tapped Proficiently balanced activity sheets tracking activities progress deliberation points evaluation radius highlights outputs primary phase visit egress collaboration agendas Client cumulative returns records capture performance illustrated collectively diarized successive setup sweetens conditions researched environments overview debriefing arcane matters turn acquaintances esteemed directives social

Research Ideas

• Training language models for improving accuracy in natural language processing applications such as question answering or dialogue systems.
• Generating new grade school math questions and answers using g...

Data from a comparative judgement survey consisting of 62 working mathematics educators (ME) at Norwegian universities or city colleges, and 57 working mathematicians at Norwegian universities. A total of 3607 comparisons of which 1780 comparisons by the ME and 1827 ME. The comparative judgement survey consisted of respondents comparing pairs of statements on mathematical definitions compiled from a literature review on mathematical definitions in the mathematics education literature. Each WM was asked to judge 40 pairs of statements with the following question: “As a researcher in mathematics, where your target group is other mathematicians, what is more important about mathematical definitions?” Each ME was asked to judge 41 pairs of statements with the following question: “For a mathematical definition in the context of teaching and learning, what is more important?” The comparative judgement was done with No More Marking software (nomoremarking.com) The data set consists of the following data: comparisons made by ME (ME.csv) comparisons made by WM (WM.csv) Look up table of codes of statements and statement formulations (key.csv) Each line in the comparison represents a comparison, where the "winner" column represents the winner and the "loser" column the loser of the comparison.

% of pupils achieving 5+ A*-Cs GCSE inc. English & Maths at Key Stage 4 (new First Entry definition) - (Snapshot) *This indicator has been discontinued due to national changes in GCSEs in 2016.

PISA Performance Scores by Country

PISA Performance Scores by Country and Year

By Dennis Kao [source]

About this dataset

The OECD PISA dataset provides performance scores for 15-year-old students in reading, mathematics, and science across OECD countries. The dataset covers the years 2000 to 2018.

These performance scores are measured using the Programme for International Student Assessment (PISA), which evaluates students' abilities to apply their knowledge and skills in reading, mathematics, and science to real-life challenges.

Reading performance is assessed based on the capacity to comprehend, use, and reflect on written texts for achieving goals, developing knowledge and potential, and participating in society.

Mathematical performance measures a student's mathematical literacy by evaluating their ability to formulate, employ, and interpret mathematics in various contexts. This includes describing, predicting, and explaining phenomena while recognizing the role that mathematics plays in the world.

Scientific performance examines a student's scientific literacy in terms of utilizing scientific knowledge to identify questions/problems/topics of interest relevant with respect to acquiring new findings/evidence/information/knowledge/content/formulation/input/output/extra-data/base/media/stats/questions/dimensions/distributions/effects/conclusions/issues/observations/trends/patterns/distribution/symptoms/hypotheses/preferences/facts/opinions/theories/beliefs/problems/causes/reasons/tests/methods/classifications/experiments/analysis/measurement/context/situations/experience/reactions/respondents/influences/emotions/perceptions/criteria/outcomes/effects/effects/significance/importance/applications/variables/models/procedures/mechanisms/concepts/spaces/types/designs/goals/models/schematics/specifications/tools/interventions/initiatives/factors/metrics/advice/sources/research/reference/background/theoretical/historical/cultural/scientific/ethical/methodological limits/rules/norms/steps/examples/practices/workflows/judgments/inferences/discoveries/disputed-effects/negative-effects/right/strength Theses skills enable them i.e., recognize claims or manipulate materials as evidence-based conclusions to address scientific phenomena and draw evidence-based conclusions about science-related issues.

The dataset includes information on the performance scores categorized by location (country alpha‑3 codes), indicator (reading, mathematical, or scientific performance), subject (boys/girls/total), and time of measurement (year). The mean score for each combination of these variables is provided in the Value column.

For more detailed information on how the dataset was collected and analyzed, please refer to the original source

How to use the dataset

Understanding the Columns

Before diving into the analysis, it is important to understand the meaning of each column in the dataset:

• LOCATION: This column represents country alpha-3 codes. OAVG indicates an average across all OECD countries.

• INDICATOR: The performance indicator being measured can be one of three options: Reading performance (PISAREAD), Mathematical performance (PISAMATH), or Scientific performance (PISASCIENCE).

• SUBJECT: This column categorizes subjects as BOY (boys), GIRL (girls), or TOT (total). It indicates which group's scores are being considered.

• TIME: The year in which the performance scores were measured can range from 2000 to 2018.

• Value: The mean score of the performance indicator for a specific subject and year is provided in this column as a floating-point number.

Getting Started with Analysis

Here are some ideas on how you can start exploring and analyzing this dataset:

• Comparing countries: You can use this dataset to compare educational performances between different countries over time for various subjects like reading, mathematics, and science.

• Subject-based analysis: You can focus on studying how gender affects students' performances by filtering data based on subject ('BOY', 'GIRL') along with years or individual countries.

• Time-based trends: Analyze trends over time by examining changes in mean scores for various indicators across years.

• ** OECD vs Non-OECD Countries**: Determine if there are significant differences in performance scores between OECD countries and non-OECD countries. You can filter the data by the LOCATION column to obtain separate datasets for each group and compare their mean scores.

Data Visualization

To enhance your understanding of the dataset, visuali...

Reading, science and math mean scores from the Pan-Canadian Assessment Program (PCAP), by province.

Data sets of strongly dampened data were analysed using SR and NLLS; the mean period and the mean confidence intervals are reported in the table (standard deviations are in brackets). The data sets were created by linear dampening the standard test data (5 days duration, hourly sampled, 24 h underlying period) in such a way that the signal reached 0 at the selected day, then uniform noise was added at 40% of the original amplitude. The results were classified to be accurate (acc) or false positives (false) depending on their period value: accurate periods were 24±0.5 h, while false positives were periods in the range (18–30 h) that were not accurate. 1) Shape of the base signal before dampening: pulse (pul) and double pulse (dblp). 2) The reported values are mean period (MP), and mean confidence intervals for accurate results (CI (acc)) and false positives (CI (false)). 3) Dampening level, represented as the day at which the initial rhythmic signal was reduced to 0, for example Dmp. 4 means that at the end of the 4th day the signal was 0 and followed by a flat line (before adding the noise).

The release of the LCA Commons Unit Process Data: field crop production Version 1.1 includes the following updates:Added meta data to reflect USDA LCA Digital Commons data submission guidance including descriptions of the process (reference to which the size of the inputs and outputs in the process relate, description of the process and technical scope and any aggregation; definition of the technology being used, its operating conditions); temporal representatives; geographic representativeness; allocation methods; process type (U: unit process, S: system process); treatment of missing intermediate flow data; treatment of missing flow data to or from the environment; intermediate flow data sources; mass balance; data treatment (description of the methods and assumptions used to transform primary and secondary data into flow quantities through recalculating, reformatting, aggregation, or proxy data and a description of data quality according to LCADC convention); sampling procedures; and review details. Also, dataset documentation and related archival publications are cited in the APA format.Changed intermediate flow categories and subcategories to reflect the ISIC International Standard Industrial Classification (ISIC).Added “US-” to the US state abbreviations for intermediate flow locations.Corrected the ISIC code for “CUTOFF domestic barge transport; average fuel” (changed to ISIC 5022: Inland freight water transport).Corrected flow names as follows: "Propachlor" renamed "Atrazine". “Bromoxynil octanoate” renamed “Bromoxynil heptanoate”. “water; plant uptake; biogenic” renamed “water; from plant uptake; biogenic” half the instances of “Benzene, pentachloronitro-“ replaced with Etridiazole and half with “Quintozene”. “CUTOFF phosphatic fertilizer, superphos. grades 22% & under; at point-of-sale” replaced with “CUTOFF phosphatic fertilizer, superphos. grades 22% and under; at point-of-sale”.Corrected flow values for “water; from plant uptake; biogenic” and “dry matter except CNPK; from plant uptake; biogenic” in some datasets.Presented data in the International Reference Life Cycle Data System (ILCD)1 format, allowing the parameterization of raw data and mathematical relations to be presented within the datasets and the inclusion of parameter uncertainty data. Note that ILCD formatted data can be converted to the ecospold v1 format using the OpenLCA software.Data quality rankings have been updated to reflect the inclusion of uncertainty data in the ILCD formatted data.Changed all parameter names to “pxxxx” to accommodate mathematical relation character limitations in OpenLCA. Also adjusted select mathematical relations to recognize zero entries. The revised list of parameter names is provided in the documentation attached.Resources in this dataset:Resource Title: Cooper-crop-production-data-parameterization-version-1.1 .File Name: Cooper-crop-production-data-parameterization-version-1.1.xlsxResource Description: Description of parameters that define the Cooper Unit process data for field crop production version 1.1Resource Title: Cooper_Crop_Data_v1.1_ILCD.File Name: Cooper_Crop_Data_v1.1_ILCD.zipResource Description: .zip archive of ILCD xml files that comprise crop production unit process modelsResource Software Recommended: openLCA,url: http://www.openlca.org/Resource Title: Summary of Revisions of the LCA Digital Commons Unit Process Data: field crop production for version 1.1 (August 2013).File Name: Summary of Revisions of the LCA Digital Commons Unit Process Data- field crop production, Version 1.1 (August 2013).pdfResource Description: Documentation of revisions to version 1 data that constitute version 1.1

The dataset includes replication materials (dataset files, python code, and the file "OSM.pdf" (Online Supplementary Materials text file + manual)) for the article entitled "Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies."

An ultrasonic phased array defect extraction method based on adaptive region growth is proposed, aiming at problems such as difficulty in defect identification and extraction caused by noise interference and complex structure of the detected object during ultrasonic phased array detection. First, bilateral filtering and grayscale processing techniques are employed for the purpose of noise reduction and initial data processing. Following this, the maximum sound pressure within the designated focusing region serves as the seed point. An adaptive region iteration method is subsequently employed to execute automatic threshold capture and region growth. In addition, mathematical morphology is applied to extract the processed defect features. In the final stage, two sets of B-scan images depicting hole defects of varying sizes are utilized for experimental validation of the proposed algorithm’s effectiveness and applicability. The defect features extracted through this algorithm are then compared and analyzed alongside the histogram threshold method, Otsu method, K-means clustering algorithm, and a modified iterative method. The results reveal that the margin of error between the measured results and the actual defect sizes is less than 13%, representing a significant enhancement in the precision of defect feature extraction. Consequently, this method establishes a dependable foundation of data for subsequent tasks, such as defect localization and quantitative and qualitative analysis.

Metadata information

Full Title Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain

Abstract

This dataset comprises results from the S34I Project, derived from processing airborne hyperspectral data acquired at the Aramo pilot site in Spain. Spectral Mapping Services (SMAPS Oy) conducted the airborne data acquisition in May 2024 using the Specim AisaFENIX sensor (covering VNIR-SWIR spectral ranges) over 17 flight lines. SMAPS performed geometric correction, radiometric calibration to reflectance, and atmospheric correction of the data. Subsequent processing steps included spectral smoothing with a Savitzky-Golay filter, cloud masking, bad pixel corrections, and hull correction (continuum removal).

Manual processing and interpretation of hyperspectral data is a challenging, time-consuming, and subjective task, necessitating automated or semi-automated approaches. Therefore, we present a semi-automated workflow for large-scale interpretation of hyperspectral data, based on a combination of state-of-the-art methodologies. This dataset results from the calculation of a series of band ratios applied to the images and their subsequent mosaicking into a TIFF file. The mosaics are delivered as georeferenced TIFF files that cover approximately 97 km² with a spatial resolution of 1.2 m per pixel. The NoData value is set to -9999, representing areas of cloud removal or missing flight lines. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.

Hyperspectral band ratios involve applying mathematical operations (such as division, subtraction, addition, or multiplication) among the reflectance values of different spectral bands. This technique enhances subtle variations in how materials absorb and reflect light across the electromagnetic spectrum. These variations are caused by electronic transitions, vibrations of chemical bonds (including -OH, Si-O, Al-O, and others), and lattice vibrations within the material's crystal structure.

By creating these mathematical combinations, specific absorption features are emphasized, generating unique spectral fingerprints for different materials. However, these fingerprints alone cannot definitively identify a mineral, as different minerals may share similar absorption features due to common chemical bonds or crystal structures. Spectral geologists use band ratios as a tool to highlight potential areas of interest, but they must integrate this information with other geological knowledge and analyses to accurately interpret the mineralogy of an area.

This dataset includes nine spectral band ratios. The mathematical formulas used to calculate each ratio are provided below:

BR1 target Carbonate / Chlorite / Epidote

BR1 = ((C7 + C9) / (C8))

C7= Mean of bands between 2246.6 and 2257.55 nm

C8= Mean of bands between 2339 and 2345 nm

C9= Mean of bands between 2400 and 2410 nm

BR2 target Chlorite

BR2 = ((Cl1 + Cl2) / (Cl2))

Cl1 = Mean of bands between 2191.93 and 2197.4 nm

Cl2 = Mean of bands between 2246.63 and 2257.55 nm

BR3 target Clay

BR3 = ((C1 + C2) / (C2))

C1 = Mean of bands between 1590.32 and 1612.56 nm

C2 = Mean of bands between 2191.93 and 2208.35 nm

BR4 target Dolomite

BR4 = ((C6 + C8) / (C7))

C6= Mean of bands between 2186 and 2191 nm

C7= Mean of bands between 2246.6 and 2257.55 nm

C8= Mean of bands between 2339 and 2345 nm

BR5 target Fe2

BR5 = ((Fe2n + Fe2d) / (Fe2d))

Fe2n = Mean of bands between 721.85 and 742.48 nm

BR6 target Fe3

BR6 = ((Fe3n - Fe3d) / (Fe3n + Fe3d))

Fe3n = Mean of bands between 776.87 to 811.26 nm

Fe3d = = Mean of 3 bands around 610 nm

BR7 target = Kaolinite / clays

BR7 = ((K1 + K2) / (K3 + K4))

K1 = Mean of bands between 2082.27 and 2104.23 nm

K2 = Mean of bands between 2104.23 and 2115.2 nm

K3 = Mean of bands between 2159.07 and 2164.55 nm

K4 = Mean of bands between 2202.88 and 2208.35 nm

BR8 target Kaolinite2 / clays

BR8 = ((K1_2 + K2_2) / (K2_2))

K1_2 = Mean of bands between 2197.4 and 2219.29 nm

K2_2 = Mean of bands between 2159.07 and 2170.03 nm

BR9 target NDVI (Normalized Difference Vegetation Index)

BR9 = ((NIR - Red) / (NIR + Red))

NIR= Mean of bands between 776.87 and 811.26 nm

Red = Mean of bands between 666.87 to 680.6 nm

Keywords Earth Observation, Remote Sensing, Hyperspestral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials

Pilot area Aramo

Language

English

URL Zenodo https://zenodo.org/uploads/14193286

Temporal reference

Acquisition date (dd.mm.yyyy) 01.05.2024

Upload date (dd.mm.yyyy) 20.11.2024

Quality and validity

Fromat GeoTiff

Spatial resolution 1.2m

Positional accuracy 0.5m

Coordinate system EPGS 4326

Access and use constrains

Use limitation None

Access constraint None

Public/Private Public

Responsible organisation

Responsible Party Beak Consultants GmbH

Responsible Contact Roberto De La Rosa

Metadata on metadata

Contact Roberto.delarosa@beak.de

Metadata language English

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

Mean difference between the accuracy of classifier in row and the classifier in column . The last column shows the mean accuracy of the respective classifier for all datasets considered in our study.

Fractional order algorithms demonstrate superior efficacy in signal processing while retaining the same level of implementation simplicity as traditional algorithms. The self-adjusting dual-stage fractional order least mean square algorithm, denoted as LFLMS, is developed to expedite convergence, improve precision, and incurring only a slight increase in computational complexity. The initial segment employs the least mean square (LMS), succeeded by the fractional LMS (FLMS) approach in the subsequent stage. The latter multiplies the LMS output, with a replica of the steering vector (Ŕ) of the intended signal. Mathematical convergence analysis and the mathematical derivation of the proposed approach are provided. Its weight adjustment integrates the conventional integer ordered gradient with a fractional-ordered. Its effectiveness is gauged through the minimization of mean square error (MSE), and thorough comparisons with alternative methods are conducted across various parameters in simulations. Simulation results underscore the superior performance of LFLMS. Notably, the convergence rate of LFLMS surpasses that of LMS by 59%, accompanied by a 49% improvement in MSE relative to LMS. So it is concluded that the LFLMS approach is a suitable choice for next generation wireless networks, including Internet of Things, 6G, radars and satellite communication.

READ ME

Welcome to the Universal Binary Principle (UBP) Dictionary System - Version 2

Author: Euan Craig, New Zealand 2025

Embark on a revolutionary journey with Version 2 of the UBP Dictionary System, a cutting-edge Python notebook that redefines how words are stored, analyzed, and visualized! Built for Kaggle, this system encodes words as multidimensional hexagonal structures in custom .hexubp files, leveraging sophisticated mathematics to integrate binary toggles, resonance frequencies, spatial coordinates, and more, all rooted in the Universal Binary Principle (UBP). This is not just a dictionary—it’s a paradigm shift in linguistic representation.

What is the UBP Dictionary System? The UBP Dictionary System transforms words into rich, vectorized representations stored in custom .hexubp files—a JSON-based format designed to encapsulate a word’s multidimensional UBP properties. Each .hexubp file represents a word as a hexagonal structure with 12 vertices, encoding: * Binary Toggles: 6-bit patterns capturing word characteristics. * Resonance Frequencies: Derived from the Schumann resonance (7.83 Hz) and UBP Pi (~2.427). * Spatial Vectors: 6D coordinates positioning words in a conceptual “Bitfield.” * Cultural and Harmonic Data: Contextual weights, waveforms, and harmonic properties.

These .hexubp files are generated, managed, and visualized through an interactive Tkinter-based interface, making the system a powerful tool for exploring language through a mathematical lens.

Unique Mathematical Foundation The UBP Dictionary System is distinguished by its deep reliance on mathematics to model language: * UBP Pi (~2.427): A custom constant derived from hexagonal geometry and resonance alignment (calculated as 6/2 * cos(2π * 7.83 * 0.318309886)), serving as the system’s foundational reference. * Resonance Frequencies: Frequencies are computed using word-specific hashes modulated by UBP Pi, with validation against the Schumann resonance (7.83 Hz ± 0.078 Hz), grounding the system in physical phenomena. * 6D Spatial Vectors: Words are positioned in a 6D Bitfield (x, y, z, time, phase, quantum state) based on toggle sums and frequency offsets, enabling spatial analysis of linguistic relationships. * GLR Validation: A non-corrective validation mechanism flags outliers in binary, frequency, and spatial data, ensuring mathematical integrity without compromising creativity.

This mathematical rigor sets the system apart from traditional dictionaries, offering a framework where words are not just strings but dynamic entities with quantifiable properties. It’s a fusion of linguistics, physics, and computational theory, inviting users to rethink language as a multidimensional phenomenon.

Comparison with Other Data Storage Mechanisms The .hexubp format is uniquely tailored for UBP’s multidimensional model. Here’s how it compares to other storage mechanisms, with metrics to highlight its strengths: CSV/JSON (Traditional Dictionaries): * Structure: Flat key-value pairs (e.g., word:definition). * Storage: ~100 bytes per word for simple text (e.g., “and”:“conjunction”). * Query Speed: O(1) for lookups, but no support for vector operations. * Limitations: Lacks multidimensional data (e.g., spatial vectors, frequencies). * .hexubp Advantage: Stores 12 vertices with vectors (~1-2 KB per word), enabling complex analyses like spatial clustering or frequency drift detection.

Relational Databases (SQL): * Structure: Tabular, with columns for word, definition, etc. * Storage: ~200-500 bytes per word, plus index overhead. * Query Speed: O(log n) for indexed queries, slower for vector computations. * Limitations: Rigid schema, inefficient for 6D vectors or dynamic vertices. * .hexubp Advantage: Lightweight, file-based (~1-2 KB per word), with JSON flexibility for UBP’s hexagonal model, no database server required.

Vector Databases (e.g., Word2Vec): * Structure: Fixed-dimension vectors (e.g., 300D for semantic embeddings). * Storage: ~2.4 KB per word (300 floats at 8 bytes each). * Query Speed: O(n) for similarity searches, optimized with indexing. * Limitations: Generic embeddings lack UBP-specific dimensions (e.g., resonance, toggles). * .hexubp Advantage: Smaller footprint (~1-2 KB), with domain-specific dimensions tailored to UBP’s theoretical framework.

Graph Databases: * Structure: Nodes and edges for word relationships. * Storage: ~500 bytes per word, plus edge overhead. * Query Speed: O(k) for traversals, where k is edge count. * Limitations: Overkill for dictionary tasks, complex setup. * .hexubp Advantage: Self-contained hexagonal structure per word, simpler for UBP’s needs, with comparable storage (~1-2 KB).

The .hexubp format balances storage efficiency, flexibility, and UBP-s...

This compressed directory contains the two data sets discussed in ‘Appendix: Data Analysis’ (above) and the scripts used to generate Figs 1 and 2. (ZIP)

Statistical summary (mean, std, 95% CI) of dIA, EE, and R under varying channel conditions.

Introduction:

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🌟 XimageNet-12 🌟

An Explainable Visual Benchmark Dataset for Robustness Evaluation. A Dataset for Image Background Exploration!

Blur Background, Segmented Background, AI-generated Background, Bias of Tools During Annotation, Color in Background, Random Background with Real Environment

+⭐ Follow Authors for project updates.

Website: XimageNet-12

Here, we trying to understand how image background effect the Computer Vision ML model, on topics such as Detection and Classification, based on baseline Li et.al work on ICLR 2022: Explainable AI: Object Recognition With Help From Background, we are now trying to enlarge the dataset, and analysis the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment! Ultimately, we also define the math equation of Robustness Scores! So if you feel interested How would we make it or join this research project? please feel free to collaborate with us!

In this paper, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. Specifically, we deliberately selected 12 categories from ImageNet, representing objects commonly encountered in practical life. To simulate real-world situations, we incorporated six diverse scenarios, such as overexposure, blurring, and color changes, etc. We further develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions, notably in relation to the background.

Progress:

• Blur Background-> Done! You can find the image Generated in the corresponding folder!
• Segmented Background -> Done! you can download the image and its corresponding transparent mask image!
• Color in Background->Done!~~ you can now download the image with different background color modified, and play with different color-ed images!
• Random Background with Real Environment -> Done! you can also find we generated the image with the photographer's real image as a background and removed the original background of the target object, but similar to the style!
• Bias of tools during annotation->Done! for this one, you won't get a new image, because this is about math and statistics data analysis when different tools and annotators are applied!
• AI generated Background-> current on progress ( 12 /12) Done!, So basically you can find one sample folder image we uploaded, please take a look at how real it is, and guess what LLM model we are using to generate the high-resolution background to make it so real :)

What tool we used to generate those images?

We employed a combination of tools and methodologies to generate the images in this dataset, ensuring both efficiency and quality in the annotation and synthesis processes.

• IoG Net: Initially, we utilized the IoG Net, which played a foundational role in our image generation pipeline.
• Polygon Faster Labeling Tool: To facilitate the annotation process, we developed a custom Polygon Faster Labeling Tool, streamlining the labeling of objects within the images.AnyLabeling Open-source Project: We also experimented with the AnyLabeling open-source project, exploring its potential for our annotation needs.
• V7 Lab Tool: Eventually, we found that the V7 Lab Tool provided the most efficient labeling speed and delivered high-quality annotations. As a result, we standardized the annotation process using this tool.
• Data Augmentation: For the synthesis of synthetic images, we relied on a combination of deep learning frameworks, including scikit-learn and OpenCV. These tools allowed us to augment and manipulate images effectively to create a diverse range of backgrounds and variations.
• GenAI: Our dataset includes images generated using the Stable Diffusion XL model, along with versions 1.5 and 2.0 of the Stable Diffusion model. These generative models played a pivotal role in crafting realistic and varied backgrounds.

For a detailed breakdown of our prompt engineering and hyperparameters, we invite you to consult our upcoming paper. This publication will provide comprehensive insights into our methodologies, enabling a deeper understanding of the image generation process.

How to use our dataset?

this dataset has been/could be downloaded via Kaggl...