*.avi; *.csv; *.txt

This paper investigates the integration of
deep learning-based volatility forecasting with portfolio
optimization strategies. We develop and evaluate a
framework that combines three neural architectures—
ResNet1D, WaveletCNN, and Temporal Convolutional
Autoencoder—with both classical mean-variance optimization and reinforcement learning approaches. Using
a comprehensive dataset spanning 2012-2025, we systematically analyze how different volatility-sentiment indicators (DIX, GEX, PCR, SKEW, VIX) and rebalancing
frequencies affect portfolio performance across eight

The STAV360 dataset is a subjective 360° video dataset.

The data set used here is a part of the Bialystok PLUS cohort study, where residents of Białystok were randomly selected from the Municipal Office database and invited to participate in the study. In this part of  study they were between 30 and 70 years old and constituted a representative sample of the local population. Data contains proteomics and atherosclerosis risk factors.

Miami Shores Golf Club Round

This dataset contains synthetically generated data representing security profiles of mobile applications, designed for training and evaluating machine learning models for vulnerability detection. It was created as part of the research described in the paper "Machine Learning-Based Vulnerability Detection in Mobile Applications" by Yanguema and Yin.

This dataset presents a curated collection of 9,000 English verbs annotated with normalized fuzzy values across four cognitive-behavioral quadrants of the BEET-M (Behavior Engagement Emotion Trigger Modes) model: Value & Credibility (NW), Relationship & Human Impact (NE), Process & Information (SE), and Time Urgency (SW). Each verb is assigned fuzzy scores summing to 1.0, along with a corresponding binary vector marking its dominant influence quadrant.

This dataset captures various aspects of parenting styles, child behavior, and family demographics to explore the relationship between caregivers’ approaches and children’s emotional and social development. It includes 22 variables covering parental age, education, number of children, emotional and disciplinary parenting behaviors, and the child's emotional responses and prosocial behaviors. Additionally, demographic factors such as the primary caretaker's gender, urban or rural environment, and generational identity (Gen X, Millennial, Gen Z) are included.

This dataset contains anonymized responses from 600 Egyptian citizens collected in March 2025 to assess public perceptions of artificial intelligence (AI) and deepfake technologies used in the animation of ancient pharaonic statues and symbols. The survey was conducted as part of a broader research study titled "Animating the Sacred: The Ethical and Cultural Implications of AI-Powered Awakening of Pharaonic Symbols Using Deepfake Techniques."

This paper explores public perceptions surrounding the use of Artificial Intelligence (AI) in cultural and media production across the Arab region. Based on a comprehensive questionnaire distributed among 2000 participants, the study investigates attitudes toward AI-driven content, ethical concerns, cultural identity threats, educational impacts, and legal responsibilities.

<p><span style="font-size: medium;">As artificial intelligence (AI) technologies rapidly integrate into cultural and media content production, questions arise about how the Arab public perceives, trusts, and engages with AI-generated content. This study investigates the perceptions of 500 participants from across the Arab world through a structured survey focusing on awareness, trust, cultural values, and the influence of algorithms on media behavior.