This dataset is about book subjects. It has 4 rows and is filtered where the books is Building a TypePad blog people want to read. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

(TR) Bu veri seti, Medium Türkiye'de yazılmış olan 203 adet makale/blog yazısını içermektedir

(ENG) This dataset contains 203 articles/blog posts written on "Medium Turkey"

• Title: Article Name
• tag: The category to which the article belongs
• number of read: Number of times the article was read
• date: Share Date

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Dataset Card for "heliosbrahma/mental_health_chatbot_dataset"

  Dataset Description





  Dataset Summary

This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from popular healthcare blogs like WebMD, Mayo Clinic and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters.

  Languages

The… See the full description on the dataset page: https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset.

Tutkimuksessa kartoitettiin suomenruotsalaisten identiteettiä ja mielipiteitä mm. arkipäivän sujumisesta, joukkoviestimien käytöstä, politiikasta ja yhteiskunnasta, arvoista, identiteetistä sekä yhteenkuuluvuudesta. Tutkimusta on rahoittanut Svenska Kulturfonden. Aluksi kysyttiin mihin kieliryhmään vastaaja ja hänen lähipiirinsä tunsivat kuuluvansa ja kuinka hyvin vastaajat katsoivat osaavansa suomea. Seuraavaksi kartoitettiin ruotsin tai suomen kielen käyttämistä eri yhteyksissä. Seuraavat kysymykset käsittelivät joukkotiedotusvälineitä. Vastaajilta kysyttiin, kuinka usein he seuraavat päivä- tai iltapäivälehtiä, kuuntelevat radiota tai katselevat televisiota. Kysyttiin myös, onko taloudessa digiboksi ja kuinka usein sähköpostia ja Internetiä kaytetään. Manner-suomalaisilta vastaajilta kysyttiin seuraavaksi kunnallisvaaleista, jotka pidettiin 26.10.2008. Kysyttiin, kuinka kiinnostuneita vastaajat olivat kunnallispolitiikasta, heille esitettiin kunnallisvaaleja koskevia väittämiä ja kysyttiin, olivatko vastaajat jollain tavalla osallisina kunnallisvaaleissa tai vaalityössä. Kysyttiin myös, mitä puoluetta aiottiin äänestää, äänestetäänkö samaa puoluetta kuin edellisissä kunnallisvaaleissa ja tiedusteltiin, mitä asioita kunnallispoliitikkojen tulisi painottaa eniten. Lisäksi esitettiin kuntia, kuntien yhdistämistä, kunnallispolitiikkaa ja kuntien palveluja käsitteleviä kysymyksiä, sekä joitakin vastaajan asuinseutua koskevia kysymyksiä. Seuraavat kysymykset oli asettanut Förvaltningslösningar språkliga konsekvenser (SpråKon) -projekti. Kysymyksillä arvioitiin käsityksiä asuinkunnan olosuhteista, mm. vanhusten- ja sairaanhoidosta, kouluista ja koulutuksesta, työllisyystilanteesta, joukkoliikenteestä, kulttuuri- ja urheilutarjonnasta, asukkaiden vaikutusmahdollisuuksista, tasa-arvon toteutumisesta, turvallisuudesta sekä kunnan yrittäjille tarjoamista mahdollisuuksista. Taustamuuttujina olivat asuinalue, ikäluokka, koulutus, työllisyystilanne, siviilisääty ja talouden koko. This study charted the identity of Swedish-speaking Finns and their opinions on various aspects of everyday life, such as politics and society, the use of mass media, leisure time activities, values, and the sense of belonging. The study was funded by the Swedish Cultural Foundation in Finland (Svenska kulturfonden). The first questions revolved around language. The respondents' and their families' Finnish and Swedish language skills were surveyed. It was examined whether the respondents had used Swedish, Finnish or both in different types of contexts, e.g. at home, at school, with friends, at bureaus and banks, and at work. The next questions concerned media, the press, radio, and television. The respondents were asked if they had a digital TV receiver in their household, and their preferred newspapers, radio stations and television channels were surveyed, as well as their internet and e-mail use. The survey also investigated the respondents' trust in different organisations such as the government, the church, social services, the universities, the police, and daycare services. The respondents' interest in local government and municipal politics was examined, and they were presented with statements concerning the upcoming 2008 municipal elections and asked whether they were involved in an election campaign either as a candidate or in some other way. It was also queried which party they usually voted for, if they would vote for the same party in this election, and whether they read blogs written by local councillors and municipal election candidates. The respondents were asked how local councillors should conduct municipal politics and if they felt that citizens are able to participate in decision-making. Some questions covered municipal mergers generally and in the respondents' own municipality. The next questions were formulated by the "Förvaltningslösningar språkliga konsekvenser" project (SpråKon). Questions covered the respondents' satisfaction in some aspects of their municipality of residence, including the availability of geriatric and healthcare services, schools and education, employment opportunities, public transport, culture and sports services, citizen participation possibilities, equal rights of men and women, safety, and opportunities for entrepreneurship. Finally, the respondents were asked whether they felt that people living in the municipality and neighbourhood -- Finnish-speaking, Swedish-speaking or in general -- share the same values, and which decision-making bodies have done the most to advance employment and entrepreneurship in the municipality. Background variables included, for instance, region of residence, age group, gender, education level, economic activity and occupational status, marital status, and household composition. Todennäköisyysotanta: yksinkertainen satunnaisotantaProbability.SimpleRandom Probability: Simple randomProbability.SimpleRandom Itsetäytettävä lomake: paperinen lomakeSelfAdministeredQuestionnaire.Paper Itsetäytettävä lomake: verkkolomakeSelfAdministeredQuestionnaire.CAWI Self-administered questionnaire: PaperSelfAdministeredQuestionnaire.Paper Self-administered questionnaire: Web-based (CAWI)SelfAdministeredQuestionnaire.CAWI

Background Information

This dataset was created as part of the World's Largest Game of Rock, Paper, Scissors talk and challenge introduced by Joseph Nelson and Salo Levy @ SXSW 2023. * https://roboflow.com/rps

* the above image is linked to the entry page

https://i.imgur.com/eVRmfw9.gif" alt="Version 11: Deploy Tab Inference"> * the demo video was prepared from the Deploy Tab, and utilizes v11 (YOLOv8n-100epochs)

The dataset includes an aggregation of images cloned from the following datasets: 1. https://universe.roboflow.com/brad-dwyer/egohands-public/ - null images 2. https://universe.roboflow.com/presentations/rock-paper-scissors-presentation/ 3. https://universe.roboflow.com/team-roboflow/rock-paper-scissors-detection 4. universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/

New images were added to the dataset and labeled to supplement the examples from the cloned datasets. Members of Team Roboflow, and more close friends of the team, are included in the dataset to assist with creating a more robust, generalized, model.

https://i.imgur.com/xIudTbe.png" alt="Example Labeled Image from the dataset: Two people playing Rock, Paper, Scissors">

Participation Rules and FAQ

* the above image is linked to the FAQ and contest entry page