Attached Image data set from combined OCT-SLO is used to train AI models and identify features to maximize quality of data set to adjust MZI reference arm, PMT Voltage of Liquid Lens and location of object. Why adjustment is needed is explained below:
We are pleased to submit our manuscript entitled "Hetero-modal Template Guide Search Region for RGBT Tracking" for consideration for publication in IEEE Transactions on Consumer Electronics. This work presents a novel framework for robust RGB-Thermal (RGBT) object tracking, addressing critical challenges in consumer electronics applications such as smart security systems, autonomous navigation, and augmented reality devices.
We are pleased to submit our manuscript entitled "Hetero-modal Template Guide Search Region for RGBT Tracking" for consideration for publication in IEEE Transactions on Consumer Electronics. This work presents a novel framework for robust RGB-Thermal (RGBT) object tracking, addressing critical challenges in consumer electronics applications such as smart security systems, autonomous navigation, and augmented reality devices.
This dataset consists of meteorological and environmental data collected in Riyadh, Saudi Arabia, over multiple years. The variables include solar radiation, temperature (both maximum and minimum in Celsius and Fahrenheit), precipitation, vapor pressure, and snow water equivalent, among others. The data spans from 2010 to the present, providing insights into solar radiation patterns, daily temperature fluctuations, and weather-related factors that can impact solar power generation. Specifically, the dataset contains the following columns:
This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements.
The shift towards cloud-native applications has been accelerating in recent years. Modern applications are increasingly distributed, taking advantage of cloud-native features such as scalability, flexibility, and high availability. However, this evolution also introduces various security challenges. From a networking perspective, the large number of interconnected components and their intricate communication patterns make detecting and mitigating traffic anomalies a complex task.
Ensemble clustering, which integrates multiple base clusterings to enhance robustness and accuracy, is commonly evaluated on over 10 benchmark datasets. These include 4 synthetic datasets (e.g., 3MC,atom,Tetra and Flame) designed to test algorithms on nonlinear separability and density variations.
SNMDat2.0 is a comprehensive multimodal dataset, expanded from the unimodal TwiBot-20, designed for Twitter social bot detection. Specifically, we add 274587 profile images and profile background images, 86498 tweet images and 49549 tweet videos based on the original 229580 twitter users, 227979 follow relationships and 33488192 tweet text.
This dataset contains 609,934 real Modbus TCP packets collected from industrial control system (ICS) environments, capturing the full byte-level structure of Modbus communication, including MBAP headers and function-specific payloads. Designed to support research in industrial cybersecurity, this dataset addresses the scarcity of diverse and realistic Modbus traffic, which often hampers the development of intrusion detection systems (IDS) and protocol-compliant synthetic data generators.
We released TrafficLLM's training datasets, which contain over 0.4M traffic data and 9K human instructions for LLM adaptation across different traffic analysis tasks.
