librarian-bots/paper-recommendations-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Brawlstars Bot V2

## Overview

Brawlstars Bot V2 is a dataset for object detection tasks - it contains Bullet annotations for 298 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Just Play Bot V2

## Overview

Just Play Bot V2 is a dataset for object detection tasks - it contains Carre annotations for 887 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

NetFlow Version 2 of the datasets is made up of 43 extended NetFlow features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Towards a Standard Feature Set for Network Intrusion Detection System Datasets, Mobile Networks and Applications, 103, 108379, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.

librarian-bots/dataset-columns dataset hosted on Hugging Face and contributed by the HF Datasets community

RoCoG-v2 (Robot Control Gestures) is a dataset intended to support the study of synthetic-to-real and ground-to-air video domain adaptation. It contains over 100K synthetically-generated videos of human avatars performing gestures from seven (7) classes. It also provides videos of real humans performing the same gestures from both ground and air perspectives

Apples Grab Robot V2

## Overview

Apples Grab Robot V2 is a dataset for object detection tasks - it contains Apples annotations for 662 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Arun garimella

Contents

NF-BoT-IoT-V2 is the extended NetFlow version of NF-BoT-IoT. Compared to the original NF-NIDS datasets, the feature set of NetFlow features has expanded from 8 to 43.

This is one dataset in the NFV2-collection by the university of Queensland aimed at standardizing network-security datasets to achieve interoperability and larger analyses.

All credit goes to the original authors: Dr. Mohanad Sarhan, Dr. Siamak Layeghy and Dr. Marius Portmann. Please cite their original journal article when using this dataset.

V1: Base dataset in CSV format as downloaded from here V2: Cleaning -> parquet files

In the parquet files all data types are already set correctly, there are 0 records with missing information and 0 duplicate records.

Here are a few use cases for this project:

1. Gaming Assistance: This model can be used as part of a gaming support system to help newcomers learn and understand different elements of the game (mod, loot, player), thus making the game more user-friendly and immersive.

2. Interactive Game Development: Game developers can utilize this model to enhance the interactivity of their games. The model can identify different classes and respond accordingly, creating dynamic and engaging gameplay experiences.

3. Content Generation for Streamers and YouTubers: This model could help streamers and YouTubers create game-related metadata to assist viewers in understanding gameplay by tagging and identifying game elements like mods, loot, and players.

4. Game Monitoring System: This model could be used in an app or tool that helps monitor gameplay, allowing users to track and visualize their interaction with mods, loot, and other players, thus helping them develop better strategies.

5. Gaming Chat Bots: This model could be used to power chat bots for gaming communities, where the bots could explain different elements of the game to players based on screenshots or live gameplay. This could be helpful for MMO games with large communities where manual moderation and assistance are resource-demanding.

This material includes a template for three tests to develop the critical ability of university students to discern between real people's accounts on twitter and accounts suspected of being exploited by bots. 1) Pretest (15'): To evaluate the level of competence of the student body, an initial test is performed with 10 Twitter accounts, of which 5 are bots, and another 5 are real accounts. They are given only the name of the profiles so that they are free to inspect it with the tool they know previously, and according to the parameters they consider appropriate. 2) Test (20'): A self-assessment test is carried out in which, in addition to a battery of 10 questions similar to the previous ones, other profiles related to the characteristics explained are included. These last ones will be exposed interactively and visually (with screenshots that focus the attention on the specific information to reflect on). The student will have to choose as many bots as he/she considers appropriate from the five options given. To identify which characteristics they found easiest to analyze, there is also an open question so that they can express themselves freely. 3) Discussion: From the final questions that appear in the previous slides, a class discussion is created to share the consequences of using bots for political purposes on Twitter and the tools we use to inform ourselves. Guiding towards the field of techno-politics. The template is available in Spanish and English. It also includes the results of the test applied to university undergraduate and master students (n=54). For more information, visit: http://mediaflows.es/alertabots/ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Con el objetivo de desarrollar la capacidad crítica de los estudiantes universitarios para discernir entre las cuentas de personas reales en twitter y las cuentas sospechosas de ser explotadas por bots, este material incluye una plantilla para la realización de tres test. 1) Pretest (15'): Para evaluar el nivel de competencia del alumnado, se realiza un test inicial con 10 cuentas de Twitter, de las cuales 5 son bots y otros 5 son cuentas reales. Se les da únicamente el nombre de la cuenta, para que ellos tengan la libertad de inspeccionarla con la herramienta que conozcan previamente y atendiendo a los parámetros que consideren oportunos (5 cuentas bot para el pretest). 2) Test (20'): Se realiza un test de autoevaluación en el que se incluyen, además de una batería de 10 preguntas similares a las anteriores, otras preguntas relacionadas con las características explicadas. Estas últimas, serán expuestas de forma interactiva y visual (con capturas de pantalla que focalizan la atención en la información concreta sobre la que reflexionar). El/la alumno/a deberá elegir tantos bots como considere oportuno de las cinco opciones que se les da (que contiene 2 bots, 2 reales y no lo sé). Asimismo, con el objetivo de identificar qué características les ha parecido más fáciles de analizar, también hay una pregunta abierta para que puedan expresarse con libertad. 3) Debate: A partir de las preguntas finales que aparecen en las diapositivas anteriores, se crea un debate en clase con el objetivo de poner en común las consecuencias que tiene el uso de bots con fines políticos en Twitter y sobre el tipo de herramientas que utilizamos para informarnos. Guiar hacia el terreno de la tecnopolítica. La plantilla está disponible en español (castellano) e inglés. Se incluye también los resultados del test aplicado a estudiantes de grado y máster universitarios (n=54). Para más información, visita: http://mediaflows.es/alertabots/

Datasets for the publication "Auditing Elon Musk's Impact on Hate Speech and Bots" [1]. File information: baseline_tweet_ids_2022.csv, hate_tweet_ids_2022.csv: List of IDs and their corresponding dates from the "baseline" and "hate" samples of tweets used in the publication, respectively. The former is used to create the number of baseline tweets each day (‘baseline_freq.csv’) while the latter is used to create the number of hate tweets each day (‘hate_freq.csv'). We share the date a tweet was made as well as its tweet ID from which you can find the original tweet’s URL with the help of this web page. As you explore these data, you may notice in a minority of cases hate tweets that are not hateful or, alternatively, baseline tweets that are hateful. This is a product of our filtering method used to collect and analyze tweets at scale. We always look forward to hearing your suggestions to improve the tweet filtering process. baseline_freq.csv, hate_freq.csv: Number of collected tweets per day for the baseline and hate samples, respectively. The file 'freq_data.py' is used to calculate these frequencies from the raw data. Feel free to consult this if you have questions about how the frequencies are calculated (or if you want to change how the data are aggregated). Use these data to recreate Figure 2 from Hickey et al [1]. See the Methods section of the publication for more details. user_hate_levels_per_day.csv: CSV file with dates (YYYY-MM-DD format) and the mean proportion of slurs used by hateful users each day from October 1st to November 30th, 2022. Use these data to recreate Figure 1 from Hickey et al [1]. See the Methods section for more details. hate_keywords.txt: Words used to query the Twitter Academic API for hate tweets. unfiltered_tweets_containing_hate_words.csv: All tweets with hate words collected with values for Perspective API attributes. Reference: Hickey, D., Schmitz, M., Fessler, D.M.T, Smaldino, P., Muric, G., & Burghardt, K. Auditing Elon Musk's Impact on Hate Speech and Bots. In Proceedings of the 17th International AAAI Conference on Web and Social Media, (2023). V2 note: This is an updated version of this dataset that was previously uploaded on 1/23/2024. In the previous version, tweet IDs were stored as floating point values which truncated them. In this version, that issue has been fixed and all IDs are complete.

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 8, "total_frames": 8464, "total_tasks": 2, "total_videos": 8, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:8" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/youliangtan/tictac-bot.

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  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More Information Needed] Paper [optional]: [More Information Needed] Demo [optional]: [More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/kingabzpro/Rick-bot-flags.

Thailand Swap Rate: BOT: Average: 2 Month data was reported at 1.290 % pa in Oct 2018. This records an increase from the previous number of 1.090 % pa for Sep 2018. Thailand Swap Rate: BOT: Average: 2 Month data is updated monthly, averaging 2.030 % pa from Jan 2005 (Median) to Oct 2018, with 166 observations. The data reached an all-time high of 5.490 % pa in Jul 2006 and a record low of 0.290 % pa in Dec 2017. Thailand Swap Rate: BOT: Average: 2 Month data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.M004: Repurchase, Swap and Liquidity Rate.

Detailed Average revenue per user (ARPU) metrics and analytics for Bloom Trading Bot, including historical data and trends.

Social bots on Twitter

Micro Machines Z Bots Mini Zs Z Force Pack Collectors Ed II 1997 Galoob MOC - Sold on eBay May 23rd, 2024 for $50.00 - Historical sales data for collectible reference.

As of December 2023, the company ASAPP was the most funded chatbot/ conversational AI worldwide, with around 380 million U.S. dollars. By contrast, the next company operating in the same field had a little over 300 million U.S. dollars.

What are AI chatbots?

A chatbot, also known as a conversational bot, is an AI software that simulates human conversation via audio or text on the internet. They are designed to answer basic questions, recommend products, and provide customer support so that organizations and companies can save manpower, money, and time. Recent developments have produced more advanced chatbots that utilize deep learning algorithms to produce answers to complex problems and questions. There are different types of chatbots, such as menu-based, keyword-based, social messaging, and voice bots. Popular chatbots are Netomi, atSpoke, and the new ChatGPT, which was launched in November 2022.

Artificial Intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic human competencies, learning from previous experiences to understand and respond to language, decisions, and problems. A growing number of companies and startups are engaging in the artificial intelligence market, which is expected to grow rapidly in the near future. Popular tech companies involved in the industry are IBM, Microsoft, and Tencent, which owns the highest number of AI and ML patent families.