The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle school and high school exams, respectively. RACE-M has 28,293 questions and RACE-H has 69,574. Each question is associated with 4 candidate answers, one of which is correct. The data generation process of RACE differs from most machine reading comprehension datasets - instead of generating questions and answers by heuristics or crowd-sourcing, questions in RACE are specifically designed for testing human reading skills, and are created by domain experts. Source: Dynamic Fusion Networks for Machine Reading Comprehension Image Source: Lai et al

RoadTracer is a dataset for extraction of road networks from aerial images. It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM. The dataset is split into a training set with 25 cities and a test set with 15 other cities. Source: RoadTracer: Automatic Extraction of Road Networks from Aerial Images Image Source: Bastani et al

Alibaba Cluster Trace captures detailed statistics for the co-located workloads of long-running and batch jobs over a course of 24 hours. The trace consists of three parts: (1) statistics of the studied homogeneous cluster of 1,313 machines, including each machine’s hardware configuration, and the runtime {CPU, Memory, Disk} resource usage for a duration of 12 hours (the 2nd half of the 24-hour period); (2) long-running job workloads, including a trace of all container deployment requests and actions, and a resource usage trace for 12 hours; (3) co-located batch job workloads, including a trace of all batch job requests and actions, and a trace of per-instance resource usage over 24 hours. It also has a second version of traces cluster-trace-v2018 that includes about 4,000 machines in a period of 8 days. Besides having larger scaler than trace-v2017, this piece trace also contains the DAG information of the production batch workloads.

BRACE is a dataset for audio-conditioned dance motion synthesis challenging common assumptions for this task: - strong music-dance correlation - controlled motion data - simple poses and movements To address these issues: - We focus on breakdancing which features acrobatic moves, tangled postures and weaker dance-music correlation. - We adopt a hybrid labelling pipeline leveraging estimation models as well as manual annotations to obtain good quality keypoint sequences at a reduced cost. - Our efforts produced the BRACE dataset, which contains over 3 hours and 30 minutes of densely annotated poses. - BRACE is also useful to fine-tune pose-estimation models thanks to its high quality keypoint annotations for complicated and uncommon poses.

Description: 1,995 People Face Images Data (Asian race). For each subject, more than 20 images per person with frontal face were collected. This data can be used for face recognition and other tasks. Data size: 1,995 people, more than 20 images per person with frontal face Race distribution: Asian people

This is a set of files representing part of the workload of Microsoft's Azure Functions offering, collected in July of 2019. This dataset is a subset of the data described in, and analyzed, in the USENIX ATC 2020 paper 'Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider'. Functions in Azure Functions are grouped into Applications. Included here is only data pertaining to a random sample of Azure Functions applications. The sampling is done per application, so that if there is data about an application in the trace, then all of its functions are included. The sampling rate is small and unspecified, but as the accompanying notebook shows, the distributions in the released trace are a good match to those in the ATC paper. In Azure Functions, applications are the unit of resource allocation. This has a few practical implications: for example, warm-up decisions are made at the application level, and memory allocation is measured by application, not by function. The 'HashOwner' field in these files is used to group applications that belong to the same subscription in Azure. It is included to indicate applications that are possibly related to each other. The dataset comprises this description, and an R notebook with plots comparing the released trace with the ATC paper, and the following sets of files: Function invocation counts and triggers Function execution time distributions Application memory allocation distributions

IOT BENIGN AND ATTACK TRACES # Data Collected for ACM SOSR 2019 # Attack & Benign Data # Instructions Flow data contains flow counters of MUD flow, each instance in the file are collected every one minute. Annotations contains information about the start, end time of the attack and corresponsing MUD flows that are impacted through the Attack. More information about the device and the attacker can be found in here Below is an example of the annotations from the Samsung smart camera. eg: "1527838552,1527839153,Localfeatures|Arpfeatures,ArpSpoof100L2D" The above line indicates that the start time of the attack to be 1527838552 and end time is 1527839153. "Localfeatures|Arpfeatures" explains that it should impact the local communication and ARP protocol. "ArpSpoof100L2D" means that the attack was arpspoof lauched with the maximum rate of 100 packets per seconds. In order to identify the attack rows in flow stats you can use below condition. "if (flowtime >= startTime*1000 and endTime*1000>=flowtime) then attack = true" -- This corresponds to the line 4470 to 4479 in the samsung smart camera. # Cite our data A. Hamza, H. Habibi Gharakheili, T. Benson, V. Sivaraman, "Detecting Volumetric Attacks on IoT Devices via SDN-Based Monitoring of MUD Activity", ACM SOSR, San Jose, California, USA, Apr 2019. # Source code https://github.com/ayyoob/mud-ie # Contact ayyoobhamza@student.unsw.edu.au

IOT TRAFFIC TRACES # Data Collected for IEEE TMC 2018 Cite our data A. Sivanathan, H. Habibi Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath and V. Sivaraman, "Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics", IEEE Transactions on Mobile Computing, Aug, 2018. Post Processing Tools https://github.com/arunmir/sdn-sim Resources List_Of_Devices.txt

The dataset contains traffic traces collected from 3 different VR applications. Researchers can use this dataset to replicate the behavior of real VR traffic directly in their studies, e.g., their simulations. Further information can be found in the repository.

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This dataset contains approximately 25,000 seismic events recorded by Observatorio Vulcanológico Andes Sur (OVDAS, SERNAGEOMIN) of four Chilean volcanoes: Nevados de Chillán Volcanic Complex (NVChVC), Villarrica (VCA), Laguna del Maule (LDM), and Puyehue-Cordón Caulle (CAU). Each event is stored as a NumPy array (.npy) of shape 14x8192. All arrays correspond to a 81.92-second (8192 samples @100Hz) window containing a single seismic event (or part of it, for longer events). The first 8 rows of each array contain data from 1 to 8 seismic stations, with stations that didn't record data filled with zero-valued arrays. The remaining 6 rows represent the true labels of the event at each sample in time: Row 9 (Class 0) represents the background class (1 for no event, 0 when an event is present). Rows 10 to 14 represent the five seismic event classes: Class 1: Volcano-Tectonic (VT) Events Class 2: Long-Period (LP) Events Class 3: Tremor (TR) Events Class 4: Avalanche (AV) Events Class 5: Ice Quake (IC) Events The dataset is broken down as follows: NVChVC: 3068 VT, 1892 LP, 2360 TR, 805 AV, and 977 IC events, recorded from January 2017 to December 2022. VCA: 1516 VT events, recorded from September 2012 to June 2023. LDM: 6663 VT events, recorded from April 2012 to July 2023. CAU: 2298 VT, 2081 LP, and 2833 TR events, recorded from April 2010 to June 2017. All seismic signals were recorded at 100 Hz. Preprocessing steps include applying a bandpass filter between 1 and 15 Hz and normalizing amplitude by dividing each signal channel by the single maximum amplitude across the 8 channels. The data was curated and manually reclassified for the article "A Framework for Real-Time Volcano-Seismic Event Recognition Based on Multi-Station Seismograms and Semantic Segmentation" (currently under review), and can be explored through codes available at: https://github.com/camilo-espinosa/volcano-seismic-segmentation. The seismic data used in this research were recorded by the Observatorio Vulcanológico de los Andes del Sur (OVDAS, https://rnvv.sernageomin.cl/observatorio-volcanologico-de-los-andes-del-sur/), part of the Servicio Nacional de Geología y Minería (SERNAGEOMIN, https://rnvv.sernageomin.cl/), Chile. Raw seismic signals for the Villarrica, Laguna del Maule, and Puyehue-Cordón Caulle volcanoes were obtained through a public information request process(https://www.consejotransparencia.cl/solicitud-informacionpublica/). The specific request codes are: Laguna del Maule (2012-2023): AS004T0005608, AS004T0004571, AS004T0004553, AS004T0006050. Puyehue-Cordón Caulle (2010-2017): AS004T0005484. Villarrica (2010-2024): AS004T0004268, AS004T0005733, AS0004T0006292, AS004T0006637. Data from Nevados del Chillán were provided through a cooperation agreement between Universidad de La Frontera and OVDAS.

PoTATO is a dataset designed to enhance the detection of floating plastic waste in aquatic environments by leveraging polarimetric imaging. It comprises 12,380 labeled images of plastic bottles, each enriched with detailed polarimetric information. The dataset aims to improve object detection accuracy under challenging outdoor lighting conditions and water surface reflections. By providing raw image data, PoTATO offers researchers the opportunity to explore novel approaches and advance state-of-the-art object detection algorithms. The dataset and associated code are publicly available

This paper describes the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains data from 27 racing sessions across the 11 scenarios with over 6.5 hours of sensor data recorded from the track. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping using the RACECAR data to explore issues that arise at the limits of operation of the vehicle.

A fully-annotated, open-design dataset of autonomous and piloted high-speed flight

The Workflow Trace Archive (WTA) is an open-access archive of workflow traces from diverse computing infrastructures. The WTA includes >48 million workflows captured from >10 computing infrastructures, representing a broad diversity of trace domains and characteristics.

fGn series used in the article to develop the simulations.

We present the Gracenote Multi-Crop (GNMC) dataset, to further research in algorithms for aesthetic image cropping. The dataset consists of a diverse collection of 10K images, each cropped in five different aspect ratios by experienced editors. GNMC is larger than existing datasets commonly used to benchmark image cropping approaches such as FCDB (1743 images) and FLMS (500 images). This dataset can enable aesthetic cropping algorithms as described in "An Experience-Based Direct Generation Approach to Automatic Image Cropping" by Christensen and Vartakavi.

This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models. Human age range from 20 to 60 years old, balance in gender, no occlusion, with head direction (<45 degree up-down and left-right). All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organizations to enable their creative and machine learning projects. For more details, please refer to the link: https://www.pixta.ai/ Or send your inquiries to contact@pixta.ai