The dataset was captured from a vineyard in Bagnolo San Vito, Italy. The dataset comprises sensor readings and high-resolution images collected from 24 March 2024 to 24 December 2024, using one high-resolution camera and a LoRaWAN network of 2 air sensors and 6 soil sensors. The sensors measured air temperature and humidity, soil dielectric permittivity, and soil temperature every 10 minutes. The camera captured one 4-Mpixel RGB image per day.
This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.
This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.
This dataset aims to support research on temporal segmentation of the Timed Up and Go (TUG) test using a first-person wearable camera. The data collection includes a training set of 8 participants and a test set of 60 participants. Among the 8 participants, the test was completed at both a normal walking pace and a simulated slower walking pace to mimic elderly movement patterns. The 60 participants were randomly divided into two groups: one group completed the test at a normal walking pace, and the other group simulated slower walking speed to mimic elderly movement patterns.
This dataset consists of images with two types of artificially added noise, intended for evaluating the robustness of machine learning models against noise perturbations. The first type of noise introduces randomly generated pixel values ranging from 0 to 255 at random positions in the image. The second type of noise adds binary noise by setting pixels at random locations to either 0 or 255. The dataset includes images with varying amounts of noisy pixels, allowing for detailed analysis under different noise intensities.
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.
Making images unlearnable through imperceptible perturbations is to prevent unauthorized image scraping from training deep neural networks (DNNs). Most existing methods for breaking these unlearnable data focus on applying image transformation techniques to disrupt the added perturbations, with limited attention given to modifying the classification tasks (target classes) of DNNs as an alternative approach.
Digital microfluidics are a unique technique for operation of nano-to-micro liter droplets based on electrowetting on dielectric. It has great application potential in the field on clinic diagnosis, life science and environment monitoring. Due to the fast droplet moving speed and high degree of freedom for droplet manipulation, it is urgent to develop automated and intelligent approaches for droplet monitoring and control.
Abstract: This dataset is sourced from anonymous health check-up records from a hospital, containing a variety of health indicators from different participants. The dataset includes basic information about the participants (such as gender, age, etc.) as well as a series of health metrics, such as blood pressure, weight, and diabetes-related indicators (e.g., urea nitrogen, blood glucose, insulin levels), along with diagnostic recommendations for each participant.
This dataset comprises a collection of CSV files containing paired time-series measurements essential for nonlinear compensation research in electrochemical seismometers (MET). Each CSV file, named according to specific magnitude-frequency combinations (magX_freqY.csv), contains two columns: 'origin' representing the original system response and 'target' representing the desired compensated output.
This dataset contains heartbeat and electromyography (EMG) signals recorded from the brachioradialis muscle under different conditions: rest and induced fatigue. It is intended for research in biomechanics, fatigue detection, and physiological signal processing. The data provide insights into muscle activity and heart rate variations, making it valuable for applications in biomedical engineering and human performance analysis.
At-sea testing of underwater acoustic communication systems requires resources unavailable to the wider research community, and researchers often resort to simplified channel models to test new protocols. The present dataset comprises in-situ hydrophone recordings of communications and channel probing waveforms, featuring an assortment of popular modulation formats. The waveforms were transmitted in three frequency bands (4-8 kHz, 9-14 kHz, and 24-32 kHz) during an overnight experiment in an enclosed fjord environment, and were recorded on two hydrophone receivers.
Photoplethysmography (PPG) is a low cost non-invasive
optical technique that can be used to assess changes in tissue
blood volume [6]. This method provides valuable information
related to the cardiovascular system. The tissue's region is
illuminated using a light source and a photodetector records
the reflected light, which varies depending on the presence
and concentration of RBCs and their oxygenation levels.
Depending on the oxygenation level of hemoglobin, RBCs
absorb light in different ways. When blood volume in the
Semantic code search, retrieving code that matches a given natural language query, is an important task to improve productivity in software engineering. Existing code search datasets face limitations: they rely on human annotators who assess code primarily through semantic understanding rather than functional verification, leading to potential inaccuracies and scalability issues. Additionally, current evaluation metrics often overlook the multi-choice nature of code search. This paper introduces CoSQA+, pairing high-quality queries from CoSQA with multiple suitable codes.
This study presents a deep learning-based framework for detecting vehicle deceleration patterns using Ultra-Wideband (UWB) Channel Impulse Response (CIR) analysis. Unlike traditional GPS or IMU-based systems, which struggle in GPS-denied environments such as tunnels, the proposed method leverages UWB CIR signal variations to classify two key driving behaviors: rapid deceleration and gradual deceleration. All data were collected from real-world experiments using UWB devices installed on actual vehicles at a professional highway testing site.
three-level network topology experimental environment was constructed based on a typical home network. An optical network terminal (Bell XE-140W-TD) was used as the starting point for Internet access, and a Gigabit wired connection was established with the core router (Huawei TC30) via a shielded Cat 6 cable. This router is a Huawei beta version that can obtain specific device information and corresponding traffic after NAT. The experimental evaluation of traffic attribution association in subsequent chapters will use this as ground truth label data for comparison.
This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.
This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.
This dataset provides detailed information and customer reviews for restaurants listed on Zomato in Bangalore, with a focus on The Nest - The Den Bengaluru, located on ITPL Main Road, Whitefield. It includes key attributes such as location, contact details, rating, cuisines offered, average cost, and detailed user-generated reviews. The dataset is ideal for sentiment analysis, customer feedback mining, restaurant recommendation systems, and hospitality service quality studies.
This dataset provides packet traces captured in a realistic 5G Vehicle-to-Everything (5G-V2X) environment, encompassing both legitimate vehicular communications and Distributed Denial of Service (DDoS) attacks. By deploying four user equipments (UEs) under multiple attacker configurations, the collected captures reflect various DDoS types (TCP SYN, UDP, and mixed) and reveal their impact on 5G-V2X networks. The dataset is further enriched with Argus files and CSV feature tables, facilitating data-driven approaches such as Machine Learning (ML)-based detection agents.
This dataset contains Challenge-Response Pairs (CRPs) from Ring Oscillator-based Physical Unclonable Functions (RO-PUFs) operating under constant and variable voltage conditions. Collected from three chips fabricated in a 22nm FDSOI process and powered by a Switched-Capacitor DC-DC converter, it includes two sections: responses at a fixed voltage and across nine voltage levels (nominal and ±10% variations). Organized into ten folders, the dataset includes 1K CRPs per chip for each voltage and a combined folder with 20K CRPs.
PassengerEEG is a brain-signal dataset designed to study how human passengers perceive and cognitively respond to potential traffic hazards in highly automated vehicles (AVs). As AVs increasingly replace human drivers, understanding passenger cognition becomes essential for improving vehicle safety and adaptive decision-making.
