FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Paper • 2104.01233 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
sbom_written: bool
total: int64
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
child 0, head: string
child 1, url: string
child 2, branch: string
child 3, sha_verified_from: string
version: string
all_passed: bool
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
schema: string
python: string
platform: string
license: string
passed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
child 1, max_lines: int64
child 2, min_lines: int64
child 1, code_lines: int64
child 2, command: stri
...
e_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
failed_ids: list<item: null>
child 0, item: null
failed: int64
root: string
redistribution: string
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
alue('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}, 'license': Value('string'), 'redistribution': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
sbom_written: bool
total: int64
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
child 0, head: string
child 1, url: string
child 2, branch: string
child 3, sha_verified_from: string
version: string
all_passed: bool
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
schema: string
python: string
platform: string
license: string
passed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
child 1, max_lines: int64
child 2, min_lines: int64
child 1, code_lines: int64
child 2, command: stri
...
e_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
failed_ids: list<item: null>
child 0, item: null
failed: int64
root: string
redistribution: string
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
alue('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}, 'license': Value('string'), 'redistribution': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
DEEP LEARNING FOR BCI
Anticloud-hardened packaging of the upstream project
DEEP_LEARNING_FOR_BCIin category BRAIN COMPUTER INTERFACE. Upstream source is vendored inUPSTREAM_CLONE/at the pinned commit below; the 12-improvement overlay lives inanticloud/. Every fact in this file traces to a file on disk in this project directory.
Category: BRAIN COMPUTER INTERFACE · Upstream: https://github.com/High-East/BCI-ToolBox · Upstream pin: 2f6e21e68157de31ee04684cb2868018ace76dac · Vendor: Anticloud FZ LLE
What This Project Does
BCI-ToolBox
1. Introduction
BCI-ToolBox is deep learning pipeline for motor-imagery classification.
This project contains five models: ShallowConvNet, DeepConvNet, EEGNet, FBCNet, BCI2021.
(BCI2021 is not an official name.)
2. Installation
Environment
- Python == 3.7.10
- PyTorch == 1.9.0
- mne == 0.23.0
- braindecode == 0.5.1
- CUDA == 11.0
Create conda environment
conda install pytorch=1.9.0 cudatoolkit=11.1 -c pytorch -c nvidia
conda install numpy pandas matplotlib pyyaml ipywidgets
pip install torchinfo braindecode moabb mne
3. Directory structure
.
├── README.md
├── base
│ ├── constructor.py
│ └── layers.py
├── configs
│ ├── BCI2021
│ │ └── default.yaml
│ ├── DeepConvNet
│ │ └── default.yaml
│ ├── EEGNet
│ │ └── default.yaml
│ ├── FBCNet
│ │ └── default.yaml
│ ├── ShallowConvNet
│ │ └── default.yaml
│ └── demo
│ ├── arch.yaml
│ ├── bci2021.yaml
│ ├── test.yaml
│ ├── train.yaml
│ └── training_params.yaml
├── data_loader
│ ├── data_generator.py
│ ├── datasets
│ │ ├── __init__.py
│ │ ├── bnci2014.py
│ │ ├── cho2017.py
│ │ ├── folder_dataset.py
│ │ ├── openbmi.py
│ │ └── tmp_dataset.py
│ └── transforms.py
├── main.py
├── models
│ ├── BCI2021
│ │ ├── BCI2021.py
│ │ └── __init__.py
│ ├── DeepConvNet
│ │ ├── DeepConvNet.py
│ │ └── __init__.py
│ ├── EEGNet
│ │ ├── EEGNet.py
│ │ └── __init__.py
│ ├── FBCNet
│ │ ├── FBCNet.py
│ │ └── __init__.py
│ ├── ShallowConvNet
│ │ ├── ShallowConvNet.py
│ │ └── __init__.py
│ ├── __init__.py
│ └── model_builder.py
├── trainers
│ ├── __init__.py
│ ├── cls_trainer.py
│ └── trainer_maker.py
└── utils
├── calculator.py
├── painter.py
└── utils.py
4. Dataset
- Use braindecode
5. Get Started
- We use W&B for experimental management tool.
- Example for W&B Dashboard
- Example for W&B Report
Create wandb_key.yaml file
- Create wandb_key.yaml file in configs directory.
# wandb_key.yaml key: WANDB API keys - WANDB API keys can be obtained from your W&B account settings.
train
Use W&B
python main.py --config_file=configs/demo/train.yaml
Not use W&B
python main.py --config_file=configs/demo/train.yaml --no_wandb
USE GPU
python main.py --config_file=configs/demo/train.yaml --device=0 # Use GPU 0
python main.py --config_file=configs/demo/train.yaml --device=1 # Use GPU 1
python main.py --config_file=configs/demo/train.yaml --device=2 # Use GPU 2
- GPU numbers depend on your server.
USE Sweep
# W&B
sweep_file: configs/demo/training_params.yaml
project: Demo
tags: [train]
- Add this block to config file for finding training parameters.
# W&B
sweep_file: configs/demo/arch.yaml
sweep_type: arch
project: Demo
tags: [train]
- Add this block to config file for finding model architecture.
test
python main.py --config_file=configs/demo/test.yaml
5. References
- ShallowConvNet [Paper] [Code]
- DeepConvNet [Paper] [Code]
- EEGNet [Paper] [Code]
- FBCNet [Paper] [Code]
- BCI2021 [Paper] [Code]
Quoted from the upstream README.md file in UPSTREAM_CLONE/.
Project-specific facts detected in this directory:
- Ecosystem: Unknown (no standard manifest detected) (manifests: none detected; scanned in UPSTREAM_CLONE)
- Top-level source layout:
base/,configs/,data_loader/,models/,trainers/,utils/ - Snapshot size: 42 files, 2587 lines of code (measured; see Benchmarks)
- Primary languages:
.py(29),.yaml(10),(none)(2),.md(1) - Upstream commit pinned for this packaging:
2f6e21e68157de31ee04684cb2868018ace76dac
Installation
No installation section was found in the upstream readme, so the commands below are generated from the manifests detected in this project directory.
# No standard manifest detected. Inspect UPSTREAM_CLONE/ for the upstream
# build system (Makefile, CMakeLists.txt, configure, ...) and follow it.
Overlay install (this project):
python -m pip install -e anticloud/ # overlay package with the 12 improvements
python anticloud/cli.py --help # 13 subcommands, JSON stdout
Usage
No usage section was found in the upstream readme. Entry points detected in this project directory:
Browse the snapshot layout listed under What This Project Does and follow the upstream run instructions for the detected ecosystem (Unknown (no standard manifest detected)).
Anticloud overlay CLI (available in every project):
python anticloud/cli.py --help # 13 subcommands, JSON stdout
python anticloud/cli.py checks # run the 16-check suite
API
- WANDB API keys can be obtained from your W&B account settings.
*Section quoted from the upstream readme.*
---
## Dependencies
| Metric | Value |
|--------|-------|
| Ecosystem | Unknown (no standard manifest detected) |
| Manifests detected | none |
| Files in snapshot | 42 |
| Lines of code | 2587 |
| Dependency references | 0 |
| Upstream license | MIT |
| Overlay license | Anticommons 0.1.0 |
Pinned lockfile: `anticloud/requirements.lock` (hash-pinned, PEP 508). SBOM: `sbom.cdx.json` (CycloneDX 1.5, pinned to the upstream SHA).
---
## Configuration
- Python == 3.7.10
- PyTorch == 1.9.0
- mne == 0.23.0
- braindecode == 0.5.1
- CUDA == 11.0
*Section quoted from the upstream readme.*
Overlay configuration (Anticloud):
- `anticloud/` - improvement overlay; environment-driven, no cloud dependency
- `LEDGERS/` - aioss tamper-evident chain files (per-project, verified with `aioss verify --live`)
- `ISOLATED_LAB_RESULTS/` - reproducibility record (environment, reproduction steps, result register, evidence)
- `OFFICIAL_BENCHMARKS/` - 26 framework assessments for this project
---
## Contributing
Upstream contributions: fork the `DEEP_LEARNING_FOR_BCI` project, create a feature branch, and open a pull request against upstream. Keep `UPSTREAM_CLONE/` untouched in this packaging; put improvements in the `anticloud/` overlay.
Overlay contributions: run the 16-check suite before opening a pull request:
```sh
python anticloud/bench/runner.py --cwd anticloud
License
Upstream license: MIT (evidence: LICENSE in the upstream snapshot).
License file excerpt:
MIT License
Copyright (c) 2022 DongHee
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
Anticommons 0.1.0 overlay
The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original MIT terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.
SPDX: MIT (upstream) + Anticommons 0.1.0 (overlay, dual).
Upstream
- Project:
DEEP_LEARNING_FOR_BCI(category: BRAIN COMPUTER INTERFACE) - Upstream URL: https://github.com/High-East/BCI-ToolBox
- Pinned commit (SHA):
2f6e21e68157de31ee04684cb2868018ace76dac - Branch: master
- Pin provenance: GitHub API commits/master. The parent-project stamp is explicitly rejected for this project.
- Snapshot location:
UPSTREAM_CLONE/(vendored, not shipped as-is) - Benchmark snapshot:
BENCH.json
Benchmarks
Measured by the Anticloud assurance suite. Every value below is read from this
project's BENCH.json, produced by a real run — the SHA3-256 of that file is
e6db6634bcb9ec49e93ace66cda85d527c6fa95da208082ef866e75ec449e8a4.
| Framework | Controls | Evidence | Coverage | Result |
|---|---|---|---|---|
| OWASP Top 10 for LLM Applications | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| OWASP Top 10 (2021) | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| SOC 2 Type II readiness | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| NIST AI Risk Management Framework | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| NIST SP 800-53 Rev. 5 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| NIST Cybersecurity Framework 2.0 | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| FedRAMP Rev. 5 | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| PCI DSS v4.0.1 | 11 controls mapped | 11 with evidence | 100.0% | PASS |
| ISO/IEC 27001:2022 | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| MITRE ATT&CK v16 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| ML Technology Readiness Level | TRL 8 | 8/8 criteria | PASS |
Overall: 16/16 checks passing.
See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.
Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the
evidence source bound to each control. This project does not claim an audit
opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are
issued by an independent assessor.
Archives and Permanent Records
| Platform | Identifier | Volume |
|---|---|---|
| Harvard Dataverse | DOI 10.7910/DVN/YMJKOG | 145 citable datasets |
| AIOSS verification kit | DOI 10.7910/DVN/OORKNJ | Offline hash verification |
| Zenodo (CERN) | — | 146 records, DOI-registered |
| OSF | — | 144 preregistered records |
| Figshare | author 20849885 | Research data and figures |
| Internet Archive | aioss-format, Anticode | Permanent binary specification |
| ORCID | 0009-0009-2233-6107 | Permanent researcher ID |
| Kaggle | pax-millennium-20 | Reproducible T4 benchmark run |
Press and Independent Publication
The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.
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