Active Speaker Detection

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

LRS3-TED is a multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research. Source: LRS3-TED: a large-scale dataset for visual speech recognition

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Yelp-Fraud is a multi-relational graph dataset built upon the Yelp spam review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models. - Dataset Statistics | # Nodes | %Fraud Nodes (Class=1) | |-------|--------| | 45,954 | 14.5 | | Relation | # Edges | |--------|--------| | R-U-R | 49,315 | | R-T-R | 573,616 | | R-S-R | 3,402,743 | | All | 3,846,979 | - Graph Construction The Yelp spam review dataset includes hotel and restaurant reviews filtered (spam) and recommended (legitimate) by Yelp. We conduct a spam review detection task on the Yelp-Fraud dataset which is a binary classification task. We take 32 handcrafted features from SpEagle paper as the raw node features for Yelp-Fraud. Based on previous studies which show that opinion fraudsters have connections in user, product, review text, and time, we take reviews as nodes in the graph and design three relations: 1) R-U-R: it connects reviews posted by the same user; 2) R-S-R: it connects reviews under the same product with the same star rating (1-5 stars); 3) R-T-R: it connects two reviews under the same product posted in the same month. To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

Bank Account Fraud (BAF) is a large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset. Source: Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models. - Dataset Statistics | # Nodes | %Fraud Nodes (Class=1)| |-------|--------| | 11,944 | 9.5 | | Relation | # Edges | |--------|--------| | U-P-U | 175,608 | | U-S-U | 3,566,479 | | U-V-U | 1,036,737 | | All | 4,398,392 | - Graph Construction The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users. To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependent cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Money laundering is a multi-billion dollar issue. Detection of laundering is very difficult. Most automated algorithms have a high false positive rate: legitimate transactions incorrectly flagged as laundering. The converse is also a major problem -- false negatives, i.e. undetected laundering transactions. Naturally, criminals work hard to cover their tracks. Access to real financial transaction data is highly restricted -- for both proprietary and privacy reasons. Even when access is possible, it is problematic to provide a correct tag (laundering or legitimate) to each transaction -- as noted above. This synthetic transaction data from IBM avoids these problems. The data provided here is based on a virtual world inhabited by individuals, companies, and banks. Individuals interact with other individuals and companies. Likewise, companies interact with other companies and with individuals. These interactions can take many forms, e.g. purchase of consumer goods and services, purchase orders for industrial supplies, payment of salaries, repayment of loans, and more. These financial transactions are generally conducted via banks, i.e. the payer and receiver both have accounts, with accounts taking multiple forms from checking to credit cards to bitcoin. Some (small) fraction of the individuals and companies in the generator model engage in criminal behavior -- such as smuggling, illegal gambling, extortion, and more. Criminals obtain funds from these illicit activities, and then try to hide the source of these illicit funds via a series of financial transactions. Such financial transactions to hide illicit funds constitute laundering. Thus, the data available here is labelled and can be used for training and testing AML (Anti Money Laundering) models and for other purposes. The data generator that created the data here not only models illicit activity, but also tracks funds derived from illicit activity through arbitrarily many transactions -- thus creating the ability to label laundering transactions many steps removed from their illicit source. With this foundation, it is straightforward for the generator to label individual transactions as laundering or legitimate.

A new fraud detection dataset FDCompCN for detecting financial statement fraud of companies in China. We construct a multi-relation graph based on the supplier, customer, shareholder, and financial information disclosed in the financial statements of Chinese companies. These data are obtained from the China Stock Market and Accounting Research (CSMAR) database. We select samples between 2020 and 2023, including 5,317 publicly listed Chinese companies traded on the Shanghai, Shenzhen, and Beijing Stock Exchanges.

The code to create the dataset is available here. The dataset used in the paper is available on github - Maker - Categorical - The brand of the vehicle. - GenModel - Categorical - The model of the vehicle. - Color - Categorical - Colour of the vehicle. - Reg_Year - Categorical - Year of Registration. - Body_Type - Categorical - Eg. SUV, Convertible. - Runned_Miles - Numerical - Distance covered by the vehicle. - Engin_Size - Categorical - Size of engine. - GearBox - Categorical - Automatic, Manual. - FuelType - Categorical - Petrol, Diesel. - Price - Numerical - Price of vehicle. - Seat_num - Numerical - Number of seats. - Door_num - Numerical - Number of Doors. - issue - Categorical - Type of damage. - issue_id - Categorical - Specific damage. - repair_complexity - Categorical - Difficulty to repair the vehicle. - repair_hours - Numerical - Time required to finish the job. - repair_cost - Numerical - Cost of repair. Other attributes are not used for evaluation in this work. breakdown_date and repair_date were added with the idea of inserting anomalies based on the number of days required to repair the vehicle.

Inpatient claims, Outpatient claims and Beneficiary details of each provider. A) Inpatient Data This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code. B) Outpatient Data This data provides details about the claims filed for those patients who visit hospitals and not admitted in it. C) Beneficiary Details Data This data contains beneficiary KYC details like health conditions,regioregion they belong to etc.

We present UniTalk, a novel dataset specifically designed for the task of active speaker detection, emphasizing challenging scenarios to enhance model generalization. Unlike previously established benchmarks such as AVA, which predominantly features old movies and thus exhibits significant domain gaps, UniTalk focuses explicitly on diverse and difficult real-world conditions. These include underrepresented languages, noisy backgrounds, and crowded scenes - such as multiple visible speakers speaking concurrently or in overlapping turns. It contains over 44.5 hours of video with frame-level active speaker annotations across 48,693 speaking identities, and spans a broad range of video types that reflect real-world conditions. Through rigorous evaluation, we show that state-of-the-art models, while achieving nearly perfect scores on AVA, fail to reach saturation on UniTalk, suggesting that the ASD task remains far from solved under realistic conditions. Nevertheless, models trained on UniTalk demonstrate stronger generalization to modern "in-the-wild" datasets like Talkies and ASW, as well as to AVA. UniTalk thus establishes a new benchmark for active speaker detection, providing researchers with a valuable resource for developing and evaluating versatile and resilient models.

The CIDII dataset is a binary classification, consisting of two classes of correct information and disinformation related to Islamic issues. The CIDII dataset belongs to our research (DISINFORMATION DETECTION ABOUT ISLAMIC ISSUES ON SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES) published in MJCS journal in the link below: https://ejournal.um.edu.my/index.php/MJCS/article/view/41935 This dataset consists of five columns: 1- ID: Each article has a unique ID. 2- Article: The article contains text that is either facts related to Islamic issues if the information is correct, or posts targeting the Islamic religion if the information is false. Most posts contain only the body without a title. 3- Propagation Source: The source refers to the source of the article content, as it contains a Facebook link in the event that the post is disinformation, or it contains a link to Islamic websites in the event that the article refers to correct information (an explanation of a verse, a hadith, or an article related to the Islamic religion). 4- Article Type: This column contains the type of article published. Is it a post if the article is disinformation, or is it an Islamic article, a Quranic interpretation, or a hadith, in case of that the information is correct? 5- Class Type: This column shows whether the article belongs to the category of correct information or disinformation.

Can you detect fraud from customer transactions? Imagine standing at the check-out counter at the grocery store with a long line behind you and the cashier not-so-quietly announces that your card has been declined. In this moment, you probably aren’t thinking about the data science that determined your fate. Embarrassed, and certain you have the funds to cover everything needed for an epic nacho party for 50 of your closest friends, you try your card again. Same result. As you step aside and allow the cashier to tend to the next customer, you receive a text message from your bank. “Press 1 if you really tried to spend $500 on cheddar cheese.” While perhaps cumbersome (and often embarrassing) in the moment, this fraud prevention system is actually saving consumers millions of dollars per year. Researchers from the IEEE Computational Intelligence Society (IEEE-CIS) want to improve this figure, while also improving the customer experience. With higher accuracy fraud detection, you can get on with your chips without the hassle. IEEE-CIS works across a variety of AI and machine learning areas, including deep neural networks, fuzzy systems, evolutionary computation, and swarm intelligence. Today they’re partnering with the world’s leading payment service company, Vesta Corporation, seeking the best solutions for fraud prevention industry, and now you are invited to join the challenge. In this competition, you’ll benchmark machine learning models on a challenging large-scale dataset. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features. You also have the opportunity to create new features to improve your results. If successful, you’ll improve the efficacy of fraudulent transaction alerts for millions of people around the world, helping hundreds of thousands of businesses reduce their fraud loss and increase their revenue. And of course, you will save party people just like you the hassle of false positives. Acknowledgements: Vesta Corporation provided the dataset for this competition. Vesta Corporation is the forerunner in guaranteed e-commerce payment solutions. Founded in 1995, Vesta pioneered the process of fully guaranteed card-not-present (CNP) payment transactions for the telecommunications industry. Since then, Vesta has firmly expanded data science and machine learning capabilities across the globe and solidified its position as the leader in guaranteed ecommerce payments. Today, Vesta guarantees more than $18B in transactions annually.