Omkar2118/datasets huggingface dataset:
MacLight
Full name: MacLight_4×4_grid

If you plan to test your method on our road network, you can find road network files in `env\map`.

- `ff.net.xml` is road network. It is unchanging, and the difference between the different environments is the traffic flow file.
- `ff_normal.rou.xml` is traffic flow file under normal pressure (Normal&Block).
- `ff_hard.rou.xml` is traffic flow file under high pressure (Peak).

It should be noted that the normal pressure we set is also relatively high.


|          | Normal&Block | Peak | arterial4x4 | grid4x4 |
|----------|--------------|------|-------------|---------|
| Vehicles | 8000         | 10286| 2485        | 1472    |


Examples to create an enviroment

Our environment interface inherits from [gymnasium](https://gymnasium.farama.org/) and [sumo-rl](https://github.com/LucasAlegre/sumo-rl), so you can easily migrate your algorithms.

1. Static environment:

   The direction of the vehicle is completely fixed and the route will not be changed.

```python
env = sumo_rl.parallel_env(
    net_file='env/map/ff.net.xml',
    route_file=f'env/map/ff_normal.rou.xml',  # Could be `ff_hard.rou.xml`
    num_seconds=args.seconds,
    use_gui=False,
    sumo_warnings=False,
    additional_sumo_cmd='--no-step-log'
)
```

2. Dynamic environment

   We define the `BlockStreet` class, which can randomly block certain roads, so that vehicles reselect the best route, which will cause sudden changes in traffic flow on certain roads.

```python
from env.wrap.random_block import BlockStreet
# block_num: Number of blocked roads, like 8
# seconds: Simulation seconds, up to 3600
env = BlockStreet(env, block_num, seconds)
```

Additionally, we strongly recommend that you set the following environment variables to get the fastest possible simulation (although it may still be slower):

```python
import os
os.environ['LIBSUMO_AS_TRACI'] = '1'
```

Introducing paper: MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
DiFF
Full name: Diffusion Facial Forgery Detection

Detecting diffusion-generated images has recently grown into an emerging research area. Existing diffusion-based datasets predominantly focus on general image generation. However, facial forgeries, which pose a more severe social risk, have remained less explored thus far. To address this gap, this paper introduces DiFF, a comprehensive dataset dedicated to face-focused diffusion-generated images. DiFF comprises over 500,000 images that are synthesized using thirteen distinct generation methods under four conditions. In particular, this dataset leverages 30,000 carefully collected textual and visual prompts, ensuring the synthesis of images with both high fidelity and semantic consistency. We conduct extensive experiments on the DiFF dataset via a human test and several representative forgery detection methods. The results demonstrate that the binary detection accuracy of both human observers and automated detectors often falls below 30%, shedding light on the challenges in detecting diffusion-generated facial forgeries. Furthermore, we propose an edge graph regularization approach to effectively enhance the generalization capability of existing detectors.

Introducing paper: Diffusion Facial Forgery Detection
VGG-Face2
Full name: Vggface2: A dataset for recognising faces  across pose and age

VGGFace2 is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession. VGGFace2 contains images from identities spanning a wide range of different ethnicities, accents, professions and ages. All face images are captured "in the wild", with pose and emotion variations and different lighting and occlusion conditions. Face distribution for different identities is varied, from 87 to 843, with an average of 362 images for each subject.

Introducing paper: VGGFace2: A dataset for recognising faces across pose and age
VGGFace2 Dataset
Full name: Vggface2: A dataset for recognising faces  across pose and age

VGGFace2 is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession. VGGFace2 contains images from identities spanning a wide range of different ethnicities, accents, professions and ages. All face images are captured "in the wild", with pose and emotion variations and different lighting and occlusion conditions. Face distribution for different identities is varied, from 87 to 843, with an average of 362 images for each subject.

Introducing paper: VGGFace2: A dataset for recognising faces across pose and age
UD_Tagalog-NewsCrawl

The Tagalog Universal Dependencies NewsCrawl dataset consists of annotated text extracted from the Leipzig Tagalog Corpus. Data included in the Leipzig Tagalog Corpus were crawled from Tagalog-language online news sites by the Leipzig University Institute for Computer Science.

The text data was automatically parsed and annotated by Angelina Aquino (University of the Philippines), and then manually corrected according the UD guidelines adapted for Tagalog by Elsie Marie Or (University of the Philippines), Maria Bardají Farré (University of Cologne), and Dr. Nikolaus Himmelmann (University of Cologne). Further verification and automated corrections were done by Lester James Miranda (Allen AI).

Due to the source of the data, several typos, grammatical errors, incomplete sentences, and Tagalog-English code-mixing can be found in the dataset.

Treebank structure

- Train: 12495 sents, 286891 tokens
- Dev: 1561 sents, 37045 tokens
- Test: 1563 sents, 36974 tokens

###Acknowledgments

Aside from the named persons in the previous section, the following also contributed to the project as manual annotators of the dataset:

- Patricia Anne Asuncion
- Paola Ellaine Luzon
- Jenard Tricano
- Mary Dianne Jamindang
- Michael Wilson Rosero
- Jim Bagano
- Yeddah Joy Piedad
- Farah Cunanan
- Calen Manzano
- Aien Gengania
- Prince Heinreich Omang
- Noah Cruz
- Leila Ysabelle Suarez
- Orlyn Joyce Esquivel
- Andre Magpantay

The annotation project was made possible by the Deutsche Forschungsgemeinschaft (DFG)-funded project titled "Information distribution and language structure - correlation of grammatical expressions of the noun/verb distinction and lexical information content in Tagalog, Indonesian and German." The DFG project team is composed of Dr. Nikolaus Himmelmann and Maria Bardají Farré from the University of Cologne, and Dr. Gerhard Heyer, Dr. Michael Richter, and Tariq Yousef from the Leipzig University.