Is Gmail Helpline Number available in Australia? This question is asking by every Gmail customer because they have many technical problem to using Gmail account now don’t worry we are offering may Gmail Customer Support Number australia helpline where users easily find the solution of their technical problem of gmail account also discuss their problem with Gmail support helpline number australia and all users who are connected with email discussion forum if you need Gmail Telephone Number australia then contact Gmail Customer Care on google. Call on - 1800-763-395

Bluesky Social Dataset

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue.

The dataset contains the complete post history of over 4M users (81% of all registered accounts), totaling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions.

Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their “like” interactions and time of bookmarking.

Dataset

Here is a description of the dataset files.

• followers.csv.gz. This compressed file contains the anonymized follower edge list. Once decompressed, each row consists of two comma-separated integers representing a directed following relation (i.e., user u follows user v).
• user_posts.tar.gz. This compressed folder contains data on the individual posts collected. Decompressing this file results in a collection of files, each containing the post of an anonymized user. Each post is stored as a JSON-formatted line.
• interactions.csv.gz. This compressed file contains the anonymized interactions edge list. Once decompressed, each row consists of six comma-separated integers representing a comment, repost, or quote interaction. These integers correspond to the following fields, in this order: user_id, replied_author, thread_root_author, reposted_author,quoted_author, and date.
• graphs.tar.gz. This compressed folder contains edge list files for the graphs emerging from reposts, quotes, and replies. Each interaction is timestamped. The folder also contains timestamped higher-order interactions emerging from discussion threads, each containing all users participating in a thread.
• feed_posts.tar.gz. This compressed folder contains posts that appear in 11 thematic feeds. Decompressing this folder results in 11 files containing posts from one feed each. Posts are stored as a JSON-formatted line. Fields are correspond to those in posts.tar.gz, except for those related to sentiment analysis (sent_label, sent_score), and reposts (repost_from, reposted_author);
• feed_bookmarks.csv. This file contains users who bookmarked any of the collected feeds. Each record contains three comma-separated values: the feed name, user id, and timestamp.
• feed_post_likes.tar.gz. This compressed folder contains data on likes to posts appearing in the feeds, one file per feed. Each record in the files contains the following information, in this order: the id of the ``liker'', the id of the post's author, the id of the liked post, and the like timestamp;
• scripts.tar.gz. A collection of Python scripts, including the ones originally used to crawl the data, and to perform experiments. These scripts are detailed in a document released within the folder.

Citation

If used for research purposes, please cite the following paper describing the dataset details:

Andrea Failla and Giulio Rossetti. "I'm in the Bluesky Tonight: Insights from a Year's Worth of Social Data." PlosOne (2024) https://doi.org/10.1371/journal.pone.0310330

Right to Erasure (Right to be forgotten)

Note: If your account was created after March 21st, 2024, or if you did not post on Bluesky before such date, no data about your account exists in the dataset. Before sending a data removal request, please make sure that you were active and posting on bluesky before March 21st, 2024.

Users included in the Bluesky Social dataset have the right to opt-out and request the removal of their data, per GDPR provisions (Article 17).

We emphasize that the released data has been thoroughly pseudonymized in compliance with GDPR (Article 4(5)). Specifically, usernames and object identifiers (e.g., URIs) have been removed, and object timestamps have been coarsened to protect individual privacy further and minimize reidentification risk. Moreover, it should be noted that the dataset was created for scientific research purposes, thereby falling under the scenarios for which GDPR provides opt-out derogations (Article 17(3)(d) and Article 89).

Nonetheless, if you wish to have your activities excluded from this dataset, please submit your request to blueskydatasetmoderation@gmail.com (with the subject "Removal request: [username]"). We will process your request within a reasonable timeframe - updates will occur monthly, if necessary, and access to previous versions will be restricted.

Acknowledgments:

This work is supported by :

• the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”,
  Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu);
• SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021;
• EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).

Here are a few use cases for this project:

1. Sports Analysis: The "Shot Tracking" model could be used by basketball teams or analysts to track a player's made-basket percentage during actual games or during practice sessions. Data can be utilized to enhance player's shooting skills, determining their most efficient areas on the court, and tracking progress over time.

2. Game Highlights: Media companies or sports broadcasters could use the model to automatically generate game highlights, focusing on successful shots. This could streamline the video editing process and make it easier to deliver exciting content to audiences quickly.

3. Virtual Coaching: In a virtual training scenario, this model can be used to provide real-time feedback to players practicing their shots. This could help players understand their strong and weak shooting zones and improve accordingly.

4. Betting & Fantasy Leagues: The model could be utilized by sports betting companies and those involved in running basketball fantasy leagues to have access to real-time data on player shooting successes. It can also help users make informed decisions.

5. Sports Equipment Manufacturing: This model can be used in the development of interactive sports equipment (e.g., smart hoops that track shooting accuracy), helping users practice and improve their shooting skills.

Here are a few use cases for this project:

1. Social Media Content Categorization - The model can be used in various social media platforms to automatically categorize images based on the content. For example, if an image contains a person, the platform may categorize it under 'People' or 'Portraits', making it easier for users to find specific types of content.

2. Advanced Security Surveillance - The model can be integrated into security systems to identify individuals in surveillance footage. This would improve security measures by allowing for accurate and quick recognition of people.

3. Health and Safety Compliance - For companies needed to ensure social distancing or count the number of people in a facility at a given time, the model could analyze CCTV footage in real-time to measure compliance.

4. Smart Photo Album Management - For personal users, the model can be used in organizing digital photo albums. By identifying the people, pictures can be automatically sorted into specific folders or albums, making it easier for users to navigate their saved images.

5. Autonomous Vehicles - The model could be integrated into the vision systems of autonomous vehicles to help detect and identify people. This would enhance pedestrian detection capabilities, making the vehicles safer.

Here are a few use cases for this project:

1. E-commerce Inventory Management: The funcaptcha model can be used in e-commerce platforms to automatically categorize products uploaded by sellers based on the objects recognized in the product images. This can significantly improve the efficiency of inventory management and product searches.

2. Trash Sorting App: An app that uses funcaptcha to help users sort their trash. By taking a picture of an item, the model could identify what the item is and tell the user how and where to dispose of it properly.

3. Home Inventory Management: Users can take pictures of their belongings, and the model can identify and catalog them. This could be useful for insurance purposes, moving, or general organization.

4. Educational Game: Developing an educational app for kids in which they can take pictures of various objects, and the app will identify what the object is, helping them learn new words and objects.

5. Assisting Visually Impaired People: funcaptcha can be used in an app that identifies objects in the environment and provides auditory feedback to assist visually impaired users in understanding their surroundings.

Here are a few use cases for this project:

1. Human Presence Detection: This computer vision model can be incorporated into security systems and smart home devices to identify the presence of humans in an area, allowing for customized responses, room automation, and improved safety.

2. Crowd Size Estimation: The "human dataset v1" can be used by event organizers or city planners to estimate the size of gatherings or crowds at public events, helping them better allocate resources and manage these events more efficiently.

3. Surveillance and Security Enhancement: The model can be integrated into video surveillance systems to more accurately identify humans, helping to filter out false alarms caused by animals and other non-human entities.

4. Collaborative Robotics: Robots equipped with this computer vision model can more easily identify and differentiate humans from their surroundings, allowing them to more effectively collaborate with people in shared spaces while ensuring human safety.

5. Smart Advertising: The "human dataset v1" can be utilized by digital signage and advertising systems to detect and count the number of human viewers, enabling targeted advertising and measuring the effectiveness of marketing campaigns.

To generate a representative dataset of real-world traffic in ISCX we defined a set of tasks, assuring that our dataset is rich enough in diversity and quantity. We created accounts for users Alice and Bob in order to use services like Skype, Facebook, etc. Below we provide the complete list of different types of traffic and applications considered in our dataset for each traffic type (VoIP, P2P, etc.)

We captured a regular session and a session over VPN, therefore we have a total of 14 traffic categories: VOIP, VPN-VOIP, P2P, VPN-P2P, etc. We also give a detailed description of the different types of traffic generated:

Browsing: Under this label we have HTTPS traffic generated by users while browsing or performing any task that includes the use of a browser. For instance, when we captured voice-calls using hangouts, even though browsing is not the main activity, we captured several browsing flows.

Email: The traffic samples generated using a Thunderbird client, and Alice and Bob Gmail accounts. The clients were configured to deliver mail through SMTP/S, and receive it using POP3/SSL in one client and IMAP/SSL in the other.

Chat: The chat label identifies instant-messaging applications. Under this label we have Facebook and Hangouts via web browsers, Skype, and IAM and ICQ using an application called pidgin [14].

Streaming: The streaming label identifies multimedia applications that require a continuous and steady stream of data. We captured traffic from Youtube (HTML5 and flash versions) and Vimeo services using Chrome and Firefox.

File Transfer: This label identifies traffic applications whose main purpose is to send or receive files and documents. For our dataset we captured Skype file transfers, FTP over SSH (SFTP) and FTP over SSL (FTPS) traffic sessions.

VoIP: The Voice over IP label groups all traffic generated by voice applications. Within this label we captured voice calls using Facebook, Hangouts and Skype.

TraP2P: This label is used to identify file-sharing protocols like Bittorrent. To generate this traffic we downloaded different .torrent files from a public a repository and captured traffic sessions using the uTorrent and Transmission applications.

The traffic was captured using Wireshark and tcpdump, generating a total amount of 28GB of data. For the VPN, we used an external VPN service provider and connected to it using OpenVPN (UDP mode). To generate SFTP and FTPS traffic we also used an external service provider and Filezilla as a client.

To facilitate the labeling process, when capturing the traffic all unnecessary services and applications were closed. (The only application executed was the objective of the capture, e.g., Skype voice-call, SFTP file transfer, etc.) We used a filter to capture only the packets with source or destination IP, the address of the local client (Alice or Bob).

The full research paper outlining the details of the dataset and its underlying principles:

Gerard Drapper Gil, Arash Habibi Lashkari, Mohammad Mamun, Ali A. Ghorbani, "Characterization of Encrypted and VPN Traffic Using Time-Related Features", In Proceedings of the 2nd International Conference on Information Systems Security and Privacy(ICISSP 2016) , pages 407-414, Rome, Italy.
ISCXFlowMeter has been written in Java for reading the pcap files and create the csv file based on selected features. The UNB ISCX Network Traffic (VPN-nonVPN) dataset consists of labeled network traffic, including full packet in pcap format and csv (flows generated by ISCXFlowMeter) also are publicly available for researchers.

For more information contact cic@unb.ca.

The UNB ISCX Network Traffic Dataset content
Traffic: Content
Web Browsing: Firefox and Chrome
Email: SMPTS, POP3S and IMAPS
Chat: ICQ, AIM, Skype, Facebook and Hangouts
Streaming: Vimeo and Youtube
File Transfer: Skype, FTPS and SFTP using Filezilla and an external service
VoIP: Facebook, Skype and Hangouts voice calls (1h duration)
P2P: uTorrent and Transmission (Bittorrent)
; cic@unb.ca.

What is the SherLock dataset?

A long-term smartphone sensor dataset with a high temporal resolution. The dataset also offers explicit labels capturing the to activity of malwares running on the devices. The dataset currently contains 10 billion data records from 30 users collected over a period of 2 years and an additional 20 users for 10 months (totaling 50 active users currently participating in the experiment).

The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. However, this dataset can be used for research in domains that are not strictly security related. For example, context aware recommender systems, event prediction, user personalization and awareness, location prediction, and more. The dataset also offers opportunities that aren't available in other datasets. For example, the dataset contains the SSID and signal strength of the connected WiFi access point (AP) which is sampled once every second, over the course of many months.

To gain full free access to the SherLock Dataset, follow these two steps:

1) Read, complete and sign the license agreement. The general restrictions are:

-The license lasts for 3 years, afterwhich the data must be deleted.

-Do not share the data with those who are not bound by the license agreement.

-Do not attempt to de-anonymize the individuals (volunteers) who have contributed the data.

-Any of your publication that benefit from the SherLock project must cite the following article: Mirsky, Yisroel, et al. "SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research." Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. ACM, 2016.

2)Send the scanned document as a PDF to bgu.sherlock@gmail.com and provide a gmail account to share a google drive folder with.

More information can be found here, or in this publication (download link).

A 2 week data sample from a single user is provided on this Kaggle page. To access the full dataset for free, please visit our site. Note: The format of the sample dataset may differ from the full dataset.

This file contains a dataset of untangled code changes, created with the help of two developers who accurately split their code changes into self contained tasks over a period of four months. The developers worked in Pharo 1, and their code changes were recorded using Epicea 2. The format of the files is STON 3, which is very similar to JSON. Contact: tinchodias@gmail.com

Here are a few use cases for this project:

1. "Agriculture Health Monitoring": Farmers and agricultural researchers could use this model to monitor and identify diseases in peach trees, allowing for early intervention and prevention of disease spread.

2. "Plant Care Mobile Application": This model could be integrated in a garden or plant care mobile app. Users could capture images of their peach tree leaves, and the app would identify any diseases, guiding the users on how to treat them.

3. "Intelligent Greenhouse Management": In a smart greenhouse setting, the model can analyze images from surveillance system regularly to detect any signs of peach tree diseases, enabling more efficient, automated care of the plants.

4. "Agricultural Drones Diagnostic Tool": Agri-tech companies could install this model in drones for aerial surveillance of large peach orchards. This would aid in rapid and large-scale detection of peach diseases, saving time and improving productivity.

5. "Educational Tool": This model could be used as an educational resource for horticulture and agriculture students to help them better understand, identify, and learn about various peach tree diseases.

Here we present datasets and Stata do-files that will allow the users to reproduce all findings in the tables and figures of the main text of the article. To do so, follow this process: (1) use the "constructing" do files to convert the raw datasets to the "constructed" datasets (in which the variables used the analyses have been generated) (2) use the "analyses" do file to conduct the analyses on these constructed datasets. Please feel free to contact Spencer Piston via email at spencerpiston@gmail.com with any questions.

KSS Dataset: Korean Single speaker Speech Dataset

KSS Dataset is designed for the Korean text-to-speech task. It consists of audio files recorded by a professional female voice actoress and their aligned text extracted from my books. As a copyright holder, by courtesy of the publishers, I release this dataset to the public. To my best knowledge, this is the first publicly available speech dataset for Korean.

File Format

Each line in transcript.txt is delimited by | into four fields.

|No.|Field|Example|

• |1|Audio File Location|1/1_0000.wav|
• |2|Original Script|그는 괜찮은 척하려고 애쓰는 것 같았다.|
• |3|Decomposed Script|그는 괜찮은 척하려고 애쓰는 것 같았다.|
• |4|Duration|3.5|

Specification

• Audio File Type: wav
• Total Running Time: 12+ hours
• Sample Rate: 44,100 KHZ
• Number of Audio Files: 12,853
• Sources
  • |1| Kyubyong Park, 500 Basic Korean Verbs, Tuttle Publishing, 2015.|
  • |2| Kyubyong Park, 500 Basic Korean Adjectives 2nd Ed., Youkrak, 2015.|
  • |3| Kyubyong Park, Essential Korean Vocabulary, Tuttle Publishing, 2015.|
  • |4| Kyubyong Park, Tuttle Learner's Korean-English Dictionary, Tuttle Publishing, 2012.|

License

NC-SA 4.0. You CANNOT use this dataset for ANY COMMERCIAL purpose. Otherwise, you can freely use this.

Citation

If you want to cite KSS Dataset, please refer to this:

Kyubyong Park, KSS Dataset: Korean Single speaker Speech Dataset, https://kaggle.com/bryanpark/korean-single-speaker-speech-dataset, 2018

Reference

Check out this for a project using this KSS Dataset.

Contact

You can contact me at kbpark.linguist@gmail.com.

April, 2018.

Kyubyong Park

Below are the links for obtaining data, code, and software in EmbSAM:ITK-SNAP-CVE for Windows/Mac/Linux is stored on figshare.ITK-SNAP-CVE's GUI data is available at: https://drive.google.com/file/d/1F_dkQG8ReiW_mh65juWKep2KMCAXuRfR/view?usp=sharingThe instruction's video for viewing GUI data are as follows: https://youtu.be/XhhtBp1kBAo?si=Q2BjYOulVxjQ7p_k and https://youtu.be/VrzfxgLTXeA?si=shfeB0J3Dn_8irBwFor 7 compressed wild-type embryos, 2 uncompressed wild-type embryos, and 20 RNAi-treated embryos:https://drive.google.com/file/d/1HIFOrZ51F_eN-dybjgZYX4RZrqsnjd7x/view?usp=sharingandhttps://drive.google.com/drive/folders/1D3vyKu2msSrdHSghHmGV6EGZuUTm5kWv?usp=sharingThe online Colab code for EmbSAM is stored at: https://drive.google.com/file/d/1CA3g2WEhPmwvSzE_QL8wBkd3nD_XWMCE/view?usp=drive_linkThe open-source code for EmbSAM is stored at:https://github.com/CunminZhao/EmbSAM.gitSystem security restrictions on personal computers may sometimes block third-party software. If this occurs with ITK-SNAP-CVE, users can simply consult the official system website [https://support.apple.com/en-us/102445] for instructions and grant the necessary permissions. This is a standard and common procedure for running third-party software on personal computers.If you have any questions, please contact Guoye Guan (guanguoye@gmail.com), Cunmin Zhao (zhaocunmin@gmail.com), and Zelin Li (zelinli6-c@my.cityu.edu.hk).

Here are a few use cases for this project:

1. Automated Retail Inventory: A "Product Recognition" model can recognize different canned products from images taken in-store and provide precise inventory data. It aids in restocking, keeping track of sales trends, and minimizing human errors in inventory management.

2. Smart Vending Machines: This model can be implemented into vending machines to automatically identify the type of canned product being dispensed. It would simplify the process of tracking and restocking inventory.

3. Waste Sorting Applications: The model can help to identify and classify different canned products in waste management centers. This can lead to better recycling strategies, aiding in differentiating recyclable material from each other quickly.

4. Grocery Shopping Apps: Users can simply take a picture of their empty cans, and the model will identify the product and automatically add it to their shopping list.

5. Consumer Behavior Analysis: Retailers can use the model in combination with store cameras to gather insights on which products are gaining attention from consumers, by recognizing the product in the customer's hand or shopping cart.

Here are a few use cases for this project:

1. E-commerce Applications: The "Guitar Boogie" model can be used by online retail shops selling musical instruments to automatically categorize and tag guitar images, making it easier for shoppers to search and find their desired guitar type.

2. Music Education & Training: Educators and trainers can create interactive applications utilizing the "Guitar Boogie" model to help students differentiate between various guitar types and learn about their specific features.

3. Social Media Content Curation: The model can be integrated into social media platforms for users who are guitar enthusiasts, enabling the platforms to generate personalized feeds or recommendations for guitar-related content based on detected guitar types.

4. Inventory Management: Music stores or warehouses can use "Guitar Boogie" to automate inventory management by scanning product images and quickly recognizing the type of guitar, thus streamlining stocking and organization processes.

5. Guitar Collection Archiving: Owners of extensive guitar collections can use the "Guitar Boogie" model to create a digital archive of their collection, easily identifying and cataloguing the specific type of each guitar. This can be useful for insurance purposes, inheritance planning, or simply for documentation and sharing with fellow guitar enthusiasts.

Here are a few use cases for this project:

1. Numismatics Studies: For individuals who are collectors or researchers in the field of numismatics (study of currency), this model could help them catalog various coins based on their class. Users can quickly input their existing collection or newly acquired coins for accurate categorization.

2. Automated Cash Register: In retail spaces, this model could be used to create automated cash registers or self-checkout stations. The system would use the computer vision model to identify the types and value of coins deposited, ensuring the correct amount of change is given.

3. Financial Institutions: Banks and other financial institutions could leverage this model to streamline their coin sorting processes. Instead of manually sorting, the computer vision model could accurately and swiftly identify and separate different kinds of coins.

4. Educational Purpose: This model can be used as a training tool in schools to teach students about different types of coins and their values. This interactive learning experience might increase their interest in understanding currencies.

5. Arcade Machines / Vending Machines: The model can be employed in arcade machines or vending machines to differentiate between various coins and authenticate the coins inserted into the machine for purchase or play.

Here are a few use cases for this project:

1. Language Learning Assistance: With the "Names" model, users can more easily learn to identify and differentiate between various word classes in the given characters set, improving their reading and pronunciation skills in the languages that use these characters.

2. Optical Character Recognition (OCR): This model can be applied to develop an OCR system for accurately detecting text and word classes in images or scanned documents, aiding transcription, data extraction, and digitization of printed materials using these characters.

3. Speech-to-Text Conversion: The "Names" model can be integrated into speech-to-text systems that handle multiple languages using the given characters set to help accurately transcribe spoken words and phrases, taking into account the identified word classes.

4. Document Analysis and Information Retrieval: Implement the model for analyzing and categorizing documents based on the identified word classes, helping to improve search results, content organization, and knowledge extraction from documents containing these characters.

5. Assistive Technologies: Utilize the "Names" model to develop tools for people with visual impairments, reading difficulties or learning disabilities, enabling them to understand and process text in languages that use the given character set more effectively.

Here are a few use cases for this project:

1. Traffic Monitoring: This model could be effectively utilized in real-time traffic monitoring systems to identify, count, and differentiate between various types of vehicles (motorcycle, car, truck, bus) and pedestrians on the road. The data obtained might be useful for traffic management and planning.

2. Autonomous Vehicles: The "test" model could be used in the development of autonomous driving systems to recognize and differentiate between various road users including motorcycles, cars, buses, trucks, and people, helping improve safety and efficiency.

3. Security Surveillance: The model can help in monitoring public spaces or private properties, identifying the type and number of different vehicles and individuals in the area. This information could be beneficial for law enforcement and security agencies.

4. Smart Parking Solutions: The "test" model could be used in developing smart parking systems, where it can identify empty parking spaces and differentiate between various vehicles. It could provide real-time updates on parking availability based on the size and type of the vehicle.

5. Traffic Violation Control: By integrating this model with surveillance systems, authorities can detect and record incidents of traffic violations, like unauthorized road crossing by pedestrians or illegal parking by various vehicles like motorcycles, cars, or trucks.