AffectNet is a large-scale facial expression database designed to advance affective computing research, particularly in both the categorical and continuous dimensional emotion models (valence and arousal). It contains over 1 million facial images collected from the internet using 1250 emotion-related keywords in six languages. Approximately 440K of these images are manually annotated with seven discrete facial expressions and the intensity of valence and arousal.

As the largest database of its kind, AffectNet supports research in automated facial expression recognition and outperforms conventional machine learning methods and commercial systems in benchmark evaluations.

DISFA and DISFA+ are benchmark databases used to detect the intensity of facial Action Units (AUs) from spontaneous facial expressions. DISFA+ includes additional annotations for valence and arousal, which are useful for modeling continuous dimensional affect.

These databases have supported numerous research studies on automatic Action Unit detection and the interpretation of facial expressions in the context of emotional responses.

RyanSpeech is a high-quality male speech corpus designed for text-to-speech (TTS) research. Unlike many publicly available TTS corpora, it features over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz, derived from real-world conversational settings. Optimized for developing TTS systems in real-world applications, RyanSpeech has been used to train state-of-the-art speech models, achieving a 3.36 mean opinion score (MOS) in its best model.

Fingerprint-based positioning is emerging as a reliable alternative to GPS and cellular localization in urban areas. However, the lack of public datasets limits research in this field. To address this, we introduce OutFin—a comprehensive outdoor fingerprint dataset collected using two smartphones. It includes WiFi, Bluetooth, and cellular signals, along with sensor data from the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. Data was gathered from 122 reference points across four distinct sites, each with unique GNSS visibility and layout. Prior to release, OutFin underwent rigorous testing to ensure data quality, making it a valuable resource for advancing fingerprint-based positioning research.