Datasets Sample Clauses

Datasets. In the following we present a short description of each datasets used for our experiments. a) Office-31: Office-31 [27] is a dataset containing 31 classes divided in 3 domains: Amazon (A), DSLR (D) and Webcam (W). Office-31 has a total of 4110 images, with a maximum of 2478 images per domain. In this dataset we use deep features extracted from the ResNet-50 architecture [28] pretrained on ImageNet.

Datasets. This Data Use Agreement (“Agreement”) is made and entered into by and between the SOUTH ALAMO REGIONAL ALLIANCE FOR THE HOMELESS (“Covered Entity”), a 501(c)(3) non-profit organization in the State of Texas, and Insert Name of Entity (“Data Recipient”), a [insert description of legal status (public / private / profit / non-profit / company incorporated in what state, LLP or LLC or other entity incorporated or registered in what state)] on (Today’s Date) for the purpose of collecting and analyzing Homeless Management Information System (HMIS) data from XXXXX.

Datasets. Pilot 1a aims to predict the maintenance status of wind turbine electrical drivetrain components, such as generators and power converters. It examines onshore and offshore wind turbines powered by a doubly-fed induction generator and provides five datasets: • La Haute-Lys dataset- Wind turbine SCADA data (SCADA-Pilot1a): This dataset is collected from various wind turbines situated in multiple wind farms. There is a unique data structure and tag name for every turbine brand. A wind turbine's Supervisory Control and Data Acquisition system contains sensor data at the most important subcomponents of the wind turbine; the collected data are analysed at 10- minute intervals. Turbines during a period where the electrical subcomponents had faults are also included. • High-frequency Data (VUB-Pilot1a): This data is derived from a dedicated measurement campaign on onshore wind turbines and consists of a limited set of electric measurements and operational parameters (e.g., wind speed). • Open wind speed dataset (Flemish-banks-data-Pilot1a): includes environmental measurements (e.g., wind speeds, wind directions) collected along the Belgian North Sea. As a basis for defining semantic labels describing wind conditions, the dataset in LLUC 1a-01 shows the typical range of wind measurements occurring in the field. • Offshore measurement campaign data (High-frequency-accelerations-Pilot1a): This dataset includes acceleration measurements, that were taken of the drivetrain of an offshore wind turbine. • Dedicated current measurement campaign data (ENGIE-VUB-Pilot1a): This dataset consists of current signals that are acquired on an onshore wind turbine. These data are similar to the La Haute Lys dataset. As such they will be merged in further discussions on data handling and analytics with the La Haute Lys data as the same processing methodology applies.

Datasets. For our experiments, we have recorded two entirely new, multi-modal datasets (cf. Fig.

Datasets. To validate the proposed approach, we have consid- ered five different benchmarks, namely: Handwritten digits dataset (MNIST), XXXXX00, XxxxxXxxx, Animals with At- tributes 2 (AwA2), Street View House Numbers (SVHN). Figure 3 shows some image samples from these data sources. MNIST [28] dataset consists of 70000 images with dimen- sion 28 28. The dataset has been split in 60000 and 10000 images for training and testing respectively. The dataset is a collection of greyscale images of handwritten numbers clas- sified among 10 classes. CIFAR10 [25] is a well known standard dataset for im- age recognition experimentation, it consists of 60000 im- ages from 10 classes of objects from different contexts. We maintain the dataset split in training and test suggested by the dataset authors: 50000 images in training set and 10000 images in test set. The images have dimension 32 32 and they are defined over three colour channels (RGB colour space). Model MNIST SVHN XXXXX00 XxxxxXXXX XxX0 Baseline CapsNet AA-Caps (Ours) 99.67% (100E) 99.34% (100E) 93.23% (100E) 92.13% (100E) 68.70% 71.60% 89.56% (50E) 89.72% (50E) 12.1% (100E) 23.97% (100E) Table 1. Summary of evaluation results. The model is validated over different bechmark to prove the contribution provided respect to the original CapsNet. We present results obtained with MNIST, SVHN, XXXXX00, XxxxxXXXX, and AwA2 datasets. CapsNet (Baseline) Conv - Primary Capsules - Final Capsules 8.2M 99.67% AA-Caps (Ours) Conv - Primary Capsules - Self-Attention - Conv 6.6M 99.34% Table 2. Comparison of CapsNet model with AA-Caps . The table presents a brief description layers that compose the structure of baseline CapsNet compared to the structure of AA-Caps, the number of trainable parameters, and the accuracy achieved by the model after 100 epochs on MNIST dataset. SmallNORB [29] consists of 24300 image 96 96 stereo grey-scale images defined over 2 colour channels. We re- sized the images to 48x48 and during training processed random 32x32 crops, and central 32x32 patch during test. AwA2 [51] consists of 37322 images of 50 animals classes. The images are collected from public sources, that makes the dataset challenging due to the uncontrolled images.

Datasets. The Parties intend to generate Datasets under the framework of open data. The Parties consider sharing Datasets necessary as it will enable the Parties to deliver expected outcomes. The purpose and the methodology for the creation of Datasets shall be described in the respective deliverables or reports. The Parties shall publicly share Datasets arising from the Project as close to real-time as possible per customary research publication norms. The Parties shall share such Datasets under the “no copyright reserved" option in the Creative Commons toolkit – CC0. If another Party’s Results or Background is to be published, such shall only occur, provided such Party has approved this beforehand.

Datasets. 6.1 Some datasets are already under the ownership of BWI. Numerous relevant datasets (with various data ownership) are already published publicly by the National Biodiversity Data Centre. Additional important datasets are in the ownership of BWI and use will be subject to appropriate permissions being received. 6.2 Datasets used by this project – see Appendix 1. 6.3 NPWS/DHLGH have provided the datasets set out in Appendix 2 and permission for all those listed there to be used for the purposes of this Project (including those of which BWI already holds a copy). 6.4 These datasets are stored on secured BWI IT infrastructure. 6.5 Access to these datasets are restricted to BWI staff involved in the project and only for the purposes outlined in this MOU. 6.6 NPWS/DHLGH have provided data at the highest resolution available for the purposes of informing the hotspot scoring.

Datasets. Section 4.11(o) of the Disclosure Schedule lists and describes all datasets that: (1) the Company considers proprietary and material to the Business; or (2) have been used within or to make available the Company Products and related services (the “Company Datasets”). The Company or its Subsidiaries have a valid legal right in and to use the Company Datasets. The Company and its Subsidiaries have obtained all necessary permissions and consents from customers to use data from the equipment and premises of such customers that are collected by, and provided to, the Company or its Subsidiaries, including through its products and services. Except as set forth in Schedule 4.11(o) of the Disclosure Schedule, the Company Products do not include or use artificial intelligence models.

Datasets. Two use cases are demonstrated in ParcBit's technological park in Palma de Mallorca, Spain. ParcBit's grid consists of a 5 km long mid-voltage network and 5 km long low-voltage network. Pilot 2B uses three datasets: • Power grid ZIV Power Meters (Power-grid-ZIV-Pilot2b): dataset consists of hourly measurements of active and reactive power conveyed to users (measured by Smart Meters), gathered by concentrator and recognized by power meter. • Transformer Sensors data (TTEMP-Pilot2b): The data is collected from eight temperature sensors installed in various parts of the transformers, two sensors for ambient temperature, humidity, and pressure, and one sensor for oil temperature. • Medium-voltage Network Analyzer (MVNA-Pilot2b): contains an Electrical Network analyzer used for current transformers.

Datasets. In order to ensure a fair comparison, we used the same data as used by [2]. The data is part of CoMon project which is a monitoring infrastructure for PlanetLab [2]. It contains CPU utilization of more than thousands of virtual machines measured from servers located at different places all around the world. The data of ten random days are chosen from the workload traces that are collected in March and April 2011. The data files contain CPU utilization values of virtual machines measured every 5 min for 24 h. Note that each line in a file represents a single request, and data for each day are combined in a single folder. In total, all the folders contain 11,746 files, which contain over 3.3 million user requests. The properties of the data are summarized in Tab.